Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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40,400 | 40,400 | ['Daniel Golovin', 'Andreas Krause', 'Matthew Streeter'] | 0908.0772v1 | Which ads should we display in sponsored search in order to maximize our
revenue? How should we dynamically rank information sources to maximize value
of information? These applications exhibit strong diminishing returns:
Selection of redundant ads and information sources decreases their marginal
utility. We show that ... | Online Learning of Assignments that Maximize Submodular Functions | 2,009 | http://arxiv.org/pdf/0908.0772v1 | Title Online Learning Assignments Maximize Submodular Functions Summary ad display sponsored search order maximize revenue dynamically rank information source maximize value information application exhibit strong diminishing return Selection redundant ad information source decrease marginal utility show problem formali... | [0.0702047273516655, 0.036722730845212936, -0.02226448431611061, -0.04861723631620407, -0.006673960480839014, -0.02619578316807747, 0.035697754472494125, 0.005653332453221083, 0.006916348822414875, -0.07342896610498428, -0.028042521327733994, 0.0046054101549088955, 0.006591056007891893, 0.07499989122152328, -0.01421258... |
40,401 | 40,401 | ['C. Balasubramanian', 'K. Duraiswamy'] | 0908.0984v1 | In real life, media information has time attributes either implicitly or
explicitly known as temporal data. This paper investigates the usefulness of
applying Bayesian classification to an interval encoded temporal database with
prioritized items. The proposed method performs temporal mining by encoding the
database wi... | An Application of Bayesian classification to Interval Encoded Temporal
mining with prioritized items | 2,009 | http://arxiv.org/pdf/0908.0984v1 | Title Application Bayesian classification Interval Encoded Temporal mining prioritized item Summary real life medium information time attribute either implicitly explicitly known temporal data paper investigates usefulness applying Bayesian classification interval encoded temporal database prioritized item proposed met... | [-0.028508933261036873, 0.03028908185660839, -0.035837572067976, -0.027262991294264793, -0.030272556468844414, 0.0138474116101861, -0.006793288514018059, 0.0297352597117424, -0.0379491001367569, -0.03716377913951874, 0.07607296854257584, 0.008877974934875965, 0.0126156285405159, 0.056196145713329315, -0.065600447356700... |
40,402 | 40,402 | ['Robert Kleinberg', 'Aleksandrs Slivkins'] | 0911.1174v1 | The Lipschitz multi-armed bandit (MAB) problem generalizes the classical
multi-armed bandit problem by assuming one is given side information consisting
of a priori upper bounds on the difference in expected payoff between certain
pairs of strategies. Classical results of (Lai and Robbins 1985) and (Auer et
al. 2002) i... | Sharp Dichotomies for Regret Minimization in Metric Spaces | 2,009 | http://arxiv.org/pdf/0911.1174v1 | Title Sharp Dichotomies Regret Minimization Metric Spaces Summary Lipschitz multiarmed bandit MAB problem generalizes classical multiarmed bandit problem assuming one given side information consisting priori upper bound difference expected payoff certain pair strategy Classical result Lai Robbins 1985 Auer et al 2002 i... | [-0.017507262527942657, 0.06138422712683678, -0.015130091458559036, -0.03270373120903969, -0.01688886433839798, -0.0056529720313847065, -0.01566641964018345, 0.03446073830127716, 0.003605370642617345, 0.030075138434767723, -0.022989902645349503, 0.005174126476049423, -0.05315357446670532, -0.009612328372895718, 0.04441... |
40,403 | 40,403 | ['Shipra Agrawal', 'Zizhuo Wang', 'Yinyu Ye'] | 0911.2974v3 | A natural optimization model that formulates many online resource allocation
and revenue management problems is the online linear program (LP) in which the
constraint matrix is revealed column by column along with the corresponding
objective coefficient. In such a model, a decision variable has to be set each
time a co... | A Dynamic Near-Optimal Algorithm for Online Linear Programming | 2,009 | http://arxiv.org/pdf/0911.2974v3 | Title Dynamic NearOptimal Algorithm Online Linear Programming Summary natural optimization model formulates many online resource allocation revenue management problem online linear program LP constraint matrix revealed column column along corresponding objective coefficient model decision variable set time column revea... | [0.026490269228816032, 0.022129103541374207, -0.028243090957403183, -0.04874974489212036, -0.011181934736669064, -0.021900618448853493, 5.994938692310825e-05, 0.032957468181848526, -0.052246857434511185, -0.018154853954911232, -0.009805353358387947, 0.05471175163984299, -0.005365205463021994, 0.10882438719272614, -0.02... |
40,404 | 40,404 | ['Iza Marfisi-Schottman', 'Aymen Sghaier', 'Sébastien George', 'Franck Tarpin-Bernard', 'Patrick Prévôt'] | 0911.4262v1 | Serious Games (SGs) have experienced a tremendous outburst these last years.
Video game companies have been producing fun, user-friendly SGs, but their
educational value has yet to be proven. Meanwhile, cognition research scientist
have been developing SGs in such a way as to guarantee an educational gain, but
the fun ... | Towards Industrialized Conception and Production of Serious Games | 2,009 | http://arxiv.org/pdf/0911.4262v1 | Title Towards Industrialized Conception Production Serious Games Summary Serious Games SGs experienced tremendous outburst last year Video game company producing fun userfriendly SGs educational value yet proven Meanwhile cognition research scientist developing SGs way guarantee educational gain fun attractive characte... | [0.030101409181952477, 0.0026686203200370073, -0.04647606611251831, -0.005047663114964962, 0.014154067263007164, -0.031771089881658554, 0.056515686213970184, -0.011564163491129875, -0.030035672709345818, 0.0013144179247319698, 0.01356200035661459, 0.05179426819086075, 0.010118752717971802, 0.12463058531284332, 0.028442... |
40,405 | 40,405 | ['Hartmut Neven', 'Vasil S. Denchev', 'Geordie Rose', 'William G. Macready'] | 0912.0779v1 | In a previous publication we proposed discrete global optimization as a
method to train a strong binary classifier constructed as a thresholded sum
over weak classifiers. Our motivation was to cast the training of a classifier
into a format amenable to solution by the quantum adiabatic algorithm. Applying
adiabatic qua... | Training a Large Scale Classifier with the Quantum Adiabatic Algorithm | 2,009 | http://arxiv.org/pdf/0912.0779v1 | Title Training Large Scale Classifier Quantum Adiabatic Algorithm Summary previous publication proposed discrete global optimization method train strong binary classifier constructed thresholded sum weak classifier motivation cast training classifier format amenable solution quantum adiabatic algorithm Applying adiabat... | [-0.024085475131869316, 0.047445010393857956, -0.032188743352890015, 0.02396136149764061, -0.008128035813570023, 0.0025820264127105474, -0.060947660356760025, 0.04676235094666481, 0.010696201585233212, -0.024722987785935402, -0.02952873706817627, -0.033422183245420456, -0.04385358840227127, -0.0035078609362244606, 0.02... |
40,406 | 40,406 | ['Shailendra Singh', 'Sanjay Silakari'] | 0912.1014v1 | Feature selection is an indispensable preprocessing step when mining huge
datasets that can significantly improve the overall system performance.
Therefore in this paper we focus on a hybrid approach of feature selection.
This method falls into two phases. The filter phase select the features with
highest information g... | An ensemble approach for feature selection of Cyber Attack Dataset | 2,009 | http://arxiv.org/pdf/0912.1014v1 | Title ensemble approach feature selection Cyber Attack Dataset Summary Feature selection indispensable preprocessing step mining huge datasets significantly improve overall system performance Therefore paper focus hybrid approach feature selection method fall two phase filter phase select feature highest information ga... | [-0.003591942135244608, 0.02275639772415161, -0.04871070012450218, 0.017847741022706032, -0.032034892588853836, -0.02174791879951954, 0.08186332136392593, -0.009611387737095356, 0.020986344665288925, -0.04730423912405968, 0.07669340074062347, 0.0073052383959293365, -0.0034109707921743393, 0.0665183961391449, -0.0090074... |
40,407 | 40,407 | ['Daniel M. Kane'] | 0912.2709v1 | We provide asymptotically sharp bounds for the Gaussian surface area and the
Gaussian noise sensitivity of polynomial threshold functions. In particular we
show that if $f$ is a degree-$d$ polynomial threshold function, then its
Gaussian sensitivity at noise rate $\epsilon$ is less than some quantity
asymptotic to $\fr... | The Gaussian Surface Area and Noise Sensitivity of Degree-$d$
Polynomials | 2,009 | http://arxiv.org/pdf/0912.2709v1 | Title Gaussian Surface Area Noise Sensitivity Degreed Polynomials Summary provide asymptotically sharp bound Gaussian surface area Gaussian noise sensitivity polynomial threshold function particular show f degreed polynomial threshold function Gaussian sensitivity noise rate epsilon le quantity asymptotic fracdsqrt2eps... | [-0.06717288494110107, 0.0016383797628805041, -0.0322284996509552, 0.08547835797071457, 0.035850897431373596, -0.02911842241883278, 0.04814301058650017, -0.0281640887260437, 0.017811670899391174, 0.00863207969814539, 0.02364012785255909, 0.02557387202978134, 0.016720090061426163, 0.058879293501377106, -0.02958033978939... |
40,408 | 40,408 | ['O. J. Oyelade', 'O. O. Oladipupo', 'I. C. Obagbuwa'] | 1002.2425v1 | The ability to monitor the progress of students academic performance is a
critical issue to the academic community of higher learning. A system for
analyzing students results based on cluster analysis and uses standard
statistical algorithms to arrange their scores data according to the level of
their performance is de... | Application of k Means Clustering algorithm for prediction of Students
Academic Performance | 2,010 | http://arxiv.org/pdf/1002.2425v1 | Title Application k Means Clustering algorithm prediction Students Academic Performance Summary ability monitor progress student academic performance critical issue academic community higher learning system analyzing student result based cluster analysis us standard statistical algorithm arrange score data according le... | [0.012255260720849037, -0.03238091245293617, -0.05472239851951599, 0.01160482782870531, 0.017057953402400017, 0.02625643089413643, 0.02851349115371704, 0.006601156201213598, 0.03528141602873802, -0.016666606068611145, 0.03313330188393593, 0.054519180208444595, 0.016980869695544243, 0.05541977286338806, -0.0089536160230... |
40,409 | 40,409 | ['Vitaly Feldman'] | 1002.3183v3 | Statistical query (SQ) learning model of Kearns (1993) is a natural
restriction of the PAC learning model in which a learning algorithm is allowed
to obtain estimates of statistical properties of the examples but cannot see
the examples themselves. We describe a new and simple characterization of the
query complexity o... | A Complete Characterization of Statistical Query Learning with
Applications to Evolvability | 2,010 | http://arxiv.org/pdf/1002.3183v3 | Title Complete Characterization Statistical Query Learning Applications Evolvability Summary Statistical query SQ learning model Kearns 1993 natural restriction PAC learning model learning algorithm allowed obtain estimate statistical property example cannot see example describe new simple characterization query comple... | [-0.021647272631525993, 0.03767754137516022, -0.018612079322338104, -0.011136188171803951, -0.04832536354660988, -0.013785481452941895, 0.00023721138131804764, 0.018488900735974312, -0.03559328615665436, -0.004494735971093178, 0.020736107602715492, 0.0024252310395240784, -0.016100913286209106, 0.06379442662000656, 0.00... |
40,410 | 40,410 | ['G. Nathiya', 'S. C. Punitha', 'M. Punithavalli'] | 1004.1743v1 | Clustering is an unsupervised learning method that constitutes a cornerstone
of an intelligent data analysis process. It is used for the exploration of
inter-relationships among a collection of patterns, by organizing them into
homogeneous clusters. Clustering has been dynamically applied to a variety of
tasks in the f... | An Analytical Study on Behavior of Clusters Using K Means, EM and K*
Means Algorithm | 2,010 | http://arxiv.org/pdf/1004.1743v1 | Title Analytical Study Behavior Clusters Using K Means EM K Means Algorithm Summary Clustering unsupervised learning method constitutes cornerstone intelligent data analysis process used exploration interrelationship among collection pattern organizing homogeneous cluster Clustering dynamically applied variety task fie... | [0.02352573536336422, -0.056265104562044144, -0.05099105089902878, 0.02970346249639988, -0.007772560231387615, 0.018338309600949287, 0.03691679239273071, 0.04018562287092209, 0.02192329429090023, -0.005122468341141939, -0.002322238404303789, 0.05109986290335655, 0.02068428508937359, 0.011929074302315712, 0.004583447705... |
40,411 | 40,411 | ['Ankur Moitra', 'Gregory Valiant'] | 1004.4223v1 | Given data drawn from a mixture of multivariate Gaussians, a basic problem is
to accurately estimate the mixture parameters. We give an algorithm for this
problem that has a running time, and data requirement polynomial in the
dimension and the inverse of the desired accuracy, with provably minimal
assumptions on the G... | Settling the Polynomial Learnability of Mixtures of Gaussians | 2,010 | http://arxiv.org/pdf/1004.4223v1 | Title Settling Polynomial Learnability Mixtures Gaussians Summary Given data drawn mixture multivariate Gaussians basic problem accurately estimate mixture parameter give algorithm problem running time data requirement polynomial dimension inverse desired accuracy provably minimal assumption Gaussians simple consequenc... | [-0.045064058154821396, 0.052726563066244125, 0.0033657762687653303, 0.031998466700315475, 0.021868102252483368, 0.007634799927473068, 0.025891361758112907, 0.017408568412065506, -0.0223164614289999, 0.03955914080142975, 0.015882831066846848, 0.02443012036383152, 0.02571365237236023, 0.030921481549739838, 0.01905695162... |
40,412 | 40,412 | ['Mikhail Belkin', 'Kaushik Sinha'] | 1004.4864v1 | The question of polynomial learnability of probability distributions,
particularly Gaussian mixture distributions, has recently received significant
attention in theoretical computer science and machine learning. However,
despite major progress, the general question of polynomial learnability of
Gaussian mixture distri... | Polynomial Learning of Distribution Families | 2,010 | http://arxiv.org/pdf/1004.4864v1 | Title Polynomial Learning Distribution Families Summary question polynomial learnability probability distribution particularly Gaussian mixture distribution recently received significant attention theoretical computer science machine learning However despite major progress general question polynomial learnability Gauss... | [-0.048455610871315, 0.013886814936995506, -0.012303175404667854, 0.022119494155049324, 0.0026823871303349733, -0.013536171987652779, -0.01847713813185692, -0.018582701683044434, -0.011211234144866467, 0.017825499176979065, 0.0573764443397522, 0.03624753654003143, 0.011723137460649014, 0.05797772482037544, 0.0055258776... |
40,413 | 40,413 | ['Adam Gauci', 'Kristian Zarb Adami', 'John Abela'] | 1005.0390v2 | In this work, decision tree learning algorithms and fuzzy inferencing systems
are applied for galaxy morphology classification. In particular, the CART, the
C4.5, the Random Forest and fuzzy logic algorithms are studied and reliable
classifiers are developed to distinguish between spiral galaxies, elliptical
galaxies o... | Machine Learning for Galaxy Morphology Classification | 2,010 | http://arxiv.org/pdf/1005.0390v2 | Title Machine Learning Galaxy Morphology Classification Summary work decision tree learning algorithm fuzzy inferencing system applied galaxy morphology classification particular CART C45 Random Forest fuzzy logic algorithm studied reliable classifier developed distinguish spiral galaxy elliptical galaxy starunknown ga... | [0.01436183787882328, -0.018092073500156403, -0.007626781240105629, 0.0038823490031063557, 0.017313862219452858, 0.033840641379356384, 0.02383428066968918, 0.04926507547497749, -0.03772355243563652, 0.0015097421128302813, 0.008867201395332813, -0.026521293446421623, 0.03182337433099747, -0.007836838252842426, -0.013818... |
40,414 | 40,414 | ['Sumit Ganguly'] | 1005.1120v2 | For each $p \in (0,2]$, we present a randomized algorithm that returns an
$\epsilon$-approximation of the $p$th frequency moment of a data stream $F_p =
\sum_{i = 1}^n \abs{f_i}^p$. The algorithm requires space $O(\epsilon^{-2} \log
(mM)(\log n))$ and processes each stream update using time $O((\log n) (\log
\epsilon^{... | Estimating small moments of data stream in nearly optimal space-time | 2,010 | http://arxiv.org/pdf/1005.1120v2 | Title Estimating small moment data stream nearly optimal spacetime Summary p 02 present randomized algorithm return epsilonapproximation pth frequency moment data stream Fp sumi 1n absfip algorithm requires space Oepsilon2 log mMlog n process stream update using time Olog n log epsilon1 nearly optimal term space lower ... | [-0.04558267444372177, 0.010708533227443695, 0.018347691744565964, -0.0022658915258944035, -0.018991196528077126, -0.03944000229239464, 0.006530310492962599, 0.024844704195857048, -0.07021108269691467, 0.03398754820227623, 0.07187191396951675, -0.03920904919505119, -0.0007672809879295528, 0.05744997039437294, -0.019592... |
40,415 | 40,415 | ['Ankan Saha', 'Ambuj Tewari'] | 1005.2146v1 | Cyclic coordinate descent is a classic optimization method that has witnessed
a resurgence of interest in machine learning. Reasons for this include its
simplicity, speed and stability, as well as its competitive performance on
$\ell_1$ regularized smooth optimization problems. Surprisingly, very little is
known about ... | On the Finite Time Convergence of Cyclic Coordinate Descent Methods | 2,010 | http://arxiv.org/pdf/1005.2146v1 | Title Finite Time Convergence Cyclic Coordinate Descent Methods Summary Cyclic coordinate descent classic optimization method witnessed resurgence interest machine learning Reasons include simplicity speed stability well competitive performance ell1 regularized smooth optimization problem Surprisingly little known fini... | [-0.01904691383242607, 0.026750218123197556, 0.009517468512058258, -0.03203791007399559, -0.010934032499790192, 0.012588400393724442, -0.015067817643284798, -0.010718051344156265, 0.03577103838324547, 0.022457823157310486, 0.019103484228253365, -0.012376347556710243, 0.020017148926854134, -0.06230856850743294, -0.00098... |
40,416 | 40,416 | ['Volker Nannen'] | 1005.2364v2 | The concept of overfitting in model selection is explained and demonstrated
with an example. After providing some background information on information
theory and Kolmogorov complexity, we provide a short explanation of Minimum
Description Length and error minimization. We conclude with a discussion of the
typical feat... | A Short Introduction to Model Selection, Kolmogorov Complexity and
Minimum Description Length (MDL) | 2,010 | http://arxiv.org/pdf/1005.2364v2 | Title Short Introduction Model Selection Kolmogorov Complexity Minimum Description Length MDL Summary concept overfitting model selection explained demonstrated example providing background information information theory Kolmogorov complexity provide short explanation Minimum Description Length error minimization concl... | [-0.04456401243805885, -0.016315944492816925, -0.01774989254772663, -0.024861404672265053, 0.005788037553429604, 0.012675956822931767, 0.0740591362118721, 0.0015853088116273284, -0.0146133191883564, 0.02477426268160343, 0.057281434535980225, 0.04798082634806633, 0.04674388840794563, 0.05345752090215683, -0.023792073130... |
40,417 | 40,417 | ['Andri Mirzal', 'Masashi Furukawa'] | 1005.2603v5 | This paper presents a concise tutorial on spectral clustering for broad
spectrum graphs which include unipartite (undirected) graph, bipartite graph,
and directed graph. We show how to transform bipartite graph and directed graph
into corresponding unipartite graph, therefore allowing a unified treatment to
all cases. ... | Eigenvectors for clustering: Unipartite, bipartite, and directed graph
cases | 2,010 | http://arxiv.org/pdf/1005.2603v5 | Title Eigenvectors clustering Unipartite bipartite directed graph case Summary paper present concise tutorial spectral clustering broad spectrum graph include unipartite undirected graph bipartite graph directed graph show transform bipartite graph directed graph corresponding unipartite graph therefore allowing unifie... | [-0.02489628829061985, -0.08898556232452393, -0.021413160488009453, 0.03332250565290451, -0.035610780119895935, 0.011725828982889652, 0.015067119151353836, -0.015249006450176239, 0.039397530257701874, -0.011923347599804401, -0.046796634793281555, -0.0014346357202157378, 0.005358168855309486, -0.013457916676998138, -0.0... |
40,418 | 40,418 | ['James P. Crutchfield', 'Sean Whalen'] | 1005.2714v2 | We introduce a theory of sequential causal inference in which learners in a
chain estimate a structural model from their upstream teacher and then pass
samples from the model to their downstream student. It extends the population
dynamics of genetic drift, recasting Kimura's selectively neutral theory as a
special case... | Structural Drift: The Population Dynamics of Sequential Learning | 2,010 | http://arxiv.org/pdf/1005.2714v2 | Title Structural Drift Population Dynamics Sequential Learning Summary introduce theory sequential causal inference learner chain estimate structural model upstream teacher pas sample model downstream student extends population dynamic genetic drift recasting Kimuras selectively neutral theory special case generalized ... | [-0.038304127752780914, 0.056641701608896255, -0.04565886780619621, -0.061517275869846344, 0.0006457430426962674, -0.03545159101486206, 0.01628267392516136, -0.01705172471702099, -0.010041468776762486, -0.0013420230243355036, 0.05282964929938316, 0.015454665757715702, 0.002656571101397276, 0.030599189922213554, 0.02678... |
40,419 | 40,419 | ['Pierre-François Marteau', 'Sylvie Gibet'] | 1005.5141v12 | This paper proposes some extensions to the work on kernels dedicated to
string or time series global alignment based on the aggregation of scores
obtained by local alignments. The extensions we propose allow to construct,
from classical recursive definition of elastic distances, recursive edit
distance (or time-warp) k... | On Recursive Edit Distance Kernels with Application to Time Series
Classification | 2,010 | http://arxiv.org/pdf/1005.5141v12 | Title Recursive Edit Distance Kernels Application Time Series Classification Summary paper proposes extension work kernel dedicated string time series global alignment based aggregation score obtained local alignment extension propose allow construct classical recursive definition elastic distance recursive edit distan... | [-0.034243084490299225, 0.026403073221445084, -0.016830019652843475, 0.019059762358665466, -0.046611763536930084, 0.003913916181772947, 0.015439056791365147, 0.06142328307032585, -0.023709937930107117, -0.02225969359278679, 0.0787755697965622, -0.009643875062465668, 0.03976677730679512, 0.039862338453531265, 0.00789438... |
40,420 | 40,420 | ['P. Bouboulis'] | 1005.5170v1 | The present report, has been inspired by the need of the author and its
colleagues to understand the underlying theory of Wirtinger's Calculus and to
further extend it to include the kernel case. The aim of the present manuscript
is twofold: a) it endeavors to provide a more rigorous presentation of the
related materia... | Wirtinger's Calculus in general Hilbert Spaces | 2,010 | http://arxiv.org/pdf/1005.5170v1 | Title Wirtingers Calculus general Hilbert Spaces Summary present report inspired need author colleague understand underlying theory Wirtingers Calculus extend include kernel case aim present manuscript twofold endeavor provide rigorous presentation related material focusing aspect author find insightful b extends notio... | [-0.021877683699131012, 0.0376695841550827, -0.008522601798176765, 0.009805820882320404, -0.03079058974981308, 0.012405401095747948, -0.020567256957292557, 0.029915183782577515, 0.005981572903692722, 0.03404664993286133, -0.03742436319589615, -0.034957434982061386, 0.006946624256670475, -0.04005247354507446, -0.0011988... |
40,421 | 40,421 | ['Aleksandrs Slivkins', 'Filip Radlinski', 'Sreenivas Gollapudi'] | 1005.5197v2 | Most learning to rank research has assumed that the utility of different
documents is independent, which results in learned ranking functions that
return redundant results. The few approaches that avoid this have rather
unsatisfyingly lacked theoretical foundations, or do not scale. We present a
learning-to-rank formul... | Ranked bandits in metric spaces: learning optimally diverse rankings
over large document collections | 2,010 | http://arxiv.org/pdf/1005.5197v2 | Title Ranked bandit metric space learning optimally diverse ranking large document collection Summary learning rank research assumed utility different document independent result learned ranking function return redundant result approach avoid rather unsatisfyingly lacked theoretical foundation scale present learningtor... | [0.04623692110180855, 0.05102129280567169, 0.0015441037248820066, 0.004123441409319639, 0.02862010896205902, 0.006811399478465319, 0.01695304363965988, 0.045195113867521286, 0.04182892292737961, -0.027121152728796005, -0.0500151701271534, -0.00902519654482603, -0.05029799044132233, 0.009584684856235981, -0.000291672768... |
40,422 | 40,422 | ['Yucheng Low', 'Joseph Gonzalez', 'Aapo Kyrola', 'Danny Bickson', 'Carlos Guestrin', 'Joseph M. Hellerstein'] | 1006.4990v1 | Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting... | GraphLab: A New Framework for Parallel Machine Learning | 2,010 | http://arxiv.org/pdf/1006.4990v1 | Title GraphLab New Framework Parallel Machine Learning Summary Designing implementing efficient provably correct parallel machine learning ML algorithm challenging Existing highlevel parallel abstraction like MapReduce insufficiently expressive lowlevel tool like MPI Pthreads leave ML expert repeatedly solving design c... | [-0.009661894291639328, 0.034596532583236694, -0.015733323991298676, -0.015012306161224842, -0.035003118216991425, 0.009322117082774639, 0.019773181527853012, -0.006333834491670132, 0.06950442492961884, 0.02598232589662075, 0.05510793253779411, 0.056912861764431, 0.0053227609023451805, 0.04073081910610199, -0.012754894... |
40,423 | 40,423 | ['Madjid Khalilian', 'Norwati Mustapha'] | 1006.5261v1 | Very large databases are required to store massive amounts of data that are
continuously inserted and queried. Analyzing huge data sets and extracting
valuable pattern in many applications are interesting for researchers. We can
identify two main groups of techniques for huge data bases mining. One group
refers to stre... | Data Stream Clustering: Challenges and Issues | 2,010 | http://arxiv.org/pdf/1006.5261v1 | Title Data Stream Clustering Challenges Issues Summary large database required store massive amount data continuously inserted queried Analyzing huge data set extracting valuable pattern many application interesting researcher identify two main group technique huge data base mining One group refers streaming data appli... | [-0.026489216834306717, 0.0018132856348529458, -0.0533626489341259, -0.007504562847316265, -0.019378935918211937, 0.029120326042175293, -0.02886327914893627, -0.007601575925946236, 0.027643028646707535, 0.008396636694669724, 0.03078877553343773, 0.02353585697710514, -0.007751514203846455, 0.06709583103656769, -0.007721... |
40,424 | 40,424 | ['Dhoha Almazro', 'Ghadeer Shahatah', 'Lamia Albdulkarim', 'Mona Kherees', 'Romy Martinez', 'William Nzoukou'] | 1006.5278v4 | Recommender systems apply data mining techniques and prediction algorithms to
predict users' interest on information, products and services among the
tremendous amount of available items. The vast growth of information on the
Internet as well as number of visitors to websites add some key challenges to
recommender syst... | A Survey Paper on Recommender Systems | 2,010 | http://arxiv.org/pdf/1006.5278v4 | Title Survey Paper Recommender Systems Summary Recommender system apply data mining technique prediction algorithm predict user interest information product service among tremendous amount available item vast growth information Internet well number visitor website add key challenge recommender system producing accurate... | [0.021156195551156998, -0.018920622766017914, -0.025686029344797134, 0.010952256619930267, -0.008208991959691048, -0.03683309629559517, 0.013347703963518143, 0.022213084623217583, 0.024835387244820595, 0.002295314334332943, -0.038629163056612015, 0.029846066609025, -0.0038689197972416878, 0.04660201072692871, -0.032441... |
40,425 | 40,425 | ['Jérôme Kunegis', 'Ernesto W. De Luca', 'Sahin Albayrak'] | 1006.5367v1 | We define and study the link prediction problem in bipartite networks,
specializing general link prediction algorithms to the bipartite case. In a
graph, a link prediction function of two vertices denotes the similarity or
proximity of the vertices. Common link prediction functions for general graphs
are defined using ... | The Link Prediction Problem in Bipartite Networks | 2,010 | http://arxiv.org/pdf/1006.5367v1 | Title Link Prediction Problem Bipartite Networks Summary define study link prediction problem bipartite network specializing general link prediction algorithm bipartite case graph link prediction function two vertex denotes similarity proximity vertex Common link prediction function general graph defined using path len... | [-0.020887264981865883, -0.013763255439698696, -0.04404943436384201, 0.024788018316030502, -0.04572137072682381, -0.059555575251579285, 0.0003048788639716804, 0.014871177263557911, 0.06635553389787674, -0.0238523930311203, -0.012899250723421574, 0.006487246137112379, -0.03370117023587227, 0.02789970301091671, 0.0270546... |
40,426 | 40,426 | ['Laurent El Ghaoui', 'Vivian Viallon', 'Tarek Rabbani'] | 1009.3515v2 | We investigate fast methods that allow to quickly eliminate variables
(features) in supervised learning problems involving a convex loss function and
a $l_1$-norm penalty, leading to a potentially substantial reduction in the
number of variables prior to running the supervised learning algorithm. The
methods are not he... | Safe Feature Elimination in Sparse Supervised Learning | 2,010 | http://arxiv.org/pdf/1009.3515v2 | Title Safe Feature Elimination Sparse Supervised Learning Summary investigate fast method allow quickly eliminate variable feature supervised learning problem involving convex loss function l1norm penalty leading potentially substantial reduction number variable prior running supervised learning algorithm method heuris... | [-0.004182683303952217, 0.019323615357279778, 0.013433349318802357, 0.026084961369633675, 0.0019009689567610621, 0.012187328189611435, 0.016926968470215797, 0.013389825820922852, -0.003949007485061884, -0.002247439231723547, 0.019117632880806923, 0.03507605567574501, -0.004972449969500303, 0.093934565782547, -0.0291411... |
40,427 | 40,427 | ['Konstantin Biatov'] | 1009.4719v1 | This paper describes an effective unsupervised speaker indexing approach. We
suggest a two stage algorithm to speed-up the state-of-the-art algorithm based
on the Bayesian Information Criterion (BIC). In the first stage of the merging
process a computationally cheap method based on the vector quantization (VQ) is
used.... | A Fast Audio Clustering Using Vector Quantization and Second Order
Statistics | 2,010 | http://arxiv.org/pdf/1009.4719v1 | Title Fast Audio Clustering Using Vector Quantization Second Order Statistics Summary paper describes effective unsupervised speaker indexing approach suggest two stage algorithm speedup stateoftheart algorithm based Bayesian Information Criterion BIC first stage merging process computationally cheap method based vecto... | [-0.07355310022830963, 0.002461535856127739, -0.023719482123851776, 0.01576410047709942, 0.009297529235482216, 0.0028405864723026752, 0.05474359169602394, 0.016290586441755295, -0.026025623083114624, 0.01948051154613495, -0.028040455654263496, 0.001579692238010466, 0.030901042744517326, -0.035311292856931686, 0.0020179... |
40,428 | 40,428 | ['S. M. Kamruzzaman', 'A. N. M. Rezaul Karim', 'Md. Saiful Islam', 'Md. Emdadul Haque'] | 1009.4972v1 | Speech recognition and speaker identification are important for
authentication and verification in security purpose, but they are difficult to
achieve. Speaker identification methods can be divided into text-independent
and text-dependent. This paper presents a technique of text-dependent speaker
identification using M... | Speaker Identification using MFCC-Domain Support Vector Machine | 2,010 | http://arxiv.org/pdf/1009.4972v1 | Title Speaker Identification using MFCCDomain Support Vector Machine Summary Speech recognition speaker identification important authentication verification security purpose difficult achieve Speaker identification method divided textindependent textdependent paper present technique textdependent speaker identification... | [-0.02607783116400242, -0.03939138352870941, 0.0004652688803616911, 0.059521060436964035, -0.013520976528525352, 0.0010644126450642943, 0.04558362439274788, -0.032348401844501495, -0.020375605672597885, -0.004435076843947172, 0.0039507425390183926, -0.010391661897301674, 0.0551946721971035, -0.015581436455249786, -0.03... |
40,429 | 40,429 | ['Matthew D. Hoffman'] | 1009.5761v1 | In certain applications it is useful to fit multinomial distributions to
observed data with a penalty term that encourages sparsity. For example, in
probabilistic latent audio source decomposition one may wish to encode the
assumption that only a few latent sources are active at any given time. The
standard heuristic o... | Approximate Maximum A Posteriori Inference with Entropic Priors | 2,010 | http://arxiv.org/pdf/1009.5761v1 | Title Approximate Maximum Posteriori Inference Entropic Priors Summary certain application useful fit multinomial distribution observed data penalty term encourages sparsity example probabilistic latent audio source decomposition one may wish encode assumption latent source active given time standard heuristic applying... | [-0.028505655005574226, 0.05937838554382324, 0.030276861041784286, -0.008063510060310364, -0.008356306701898575, -0.01043867040425539, 0.002014602767303586, 0.027927594259381294, -0.10546406358480453, -0.0030313758179545403, -0.028950681909918785, 0.02949124574661255, 0.026805799454450607, -0.03663402795791626, 0.01931... |
40,430 | 40,430 | ['Sudipto Guha', 'Kamesh Munagala', 'Martin Pal'] | 1011.1161v3 | In this paper we initiate the study of optimization of bandit type problems
in scenarios where the feedback of a play is not immediately known. This arises
naturally in allocation problems which have been studied extensively in the
literature, albeit in the absence of delays in the feedback. We study this
problem in th... | Multiarmed Bandit Problems with Delayed Feedback | 2,010 | http://arxiv.org/pdf/1011.1161v3 | Title Multiarmed Bandit Problems Delayed Feedback Summary paper initiate study optimization bandit type problem scenario feedback play immediately known arises naturally allocation problem studied extensively literature albeit absence delay feedback study problem Bayesian setting presence delay solution provable guaran... | [0.03368421643972397, 0.06928133219480515, -0.021639585494995117, -0.03520025312900543, -0.004692803602665663, -0.03717969357967377, 0.036099065095186234, 0.012210307642817497, -0.0208603348582983, -0.006836640182882547, 0.0051446715369820595, -0.006070876494050026, -0.05805251747369766, 0.0240118820220232, 0.016658492... |
40,431 | 40,431 | ['Jacob Abernethy', 'Peter L. Bartlett', 'Elad Hazan'] | 1011.1936v1 | We consider the celebrated Blackwell Approachability Theorem for two-player
games with vector payoffs. We show that Blackwell's result is equivalent, via
efficient reductions, to the existence of "no-regret" algorithms for Online
Linear Optimization. Indeed, we show that any algorithm for one such problem
can be effici... | Blackwell Approachability and Low-Regret Learning are Equivalent | 2,010 | http://arxiv.org/pdf/1011.1936v1 | Title Blackwell Approachability LowRegret Learning Equivalent Summary consider celebrated Blackwell Approachability Theorem twoplayer game vector payoff show Blackwells result equivalent via efficient reduction existence noregret algorithm Online Linear Optimization Indeed show algorithm one problem efficiently convert... | [-0.025725586339831352, 0.05888979509472847, -0.03264343738555908, -0.01007288321852684, -0.026690054684877396, -0.043660104274749756, -0.006411109119653702, 0.0330372154712677, -0.02700350247323513, 0.04223168268799782, 0.004927211906760931, 0.0478442907333374, -0.012339211069047451, 0.019279396161437035, -0.013761328... |
40,432 | 40,432 | ['Ofer Dekel', 'Ran Gilad-Bachrach', 'Ohad Shamir', 'Lin Xiao'] | 1012.1370v1 | The standard model of online prediction deals with serial processing of
inputs by a single processor. However, in large-scale online prediction
problems, where inputs arrive at a high rate, an increasingly common necessity
is to distribute the computation across several processors. A non-trivial
challenge is to design ... | Robust Distributed Online Prediction | 2,010 | http://arxiv.org/pdf/1012.1370v1 | Title Robust Distributed Online Prediction Summary standard model online prediction deal serial processing input single processor However largescale online prediction problem input arrive high rate increasingly common necessity distribute computation across several processor nontrivial challenge design distributed algo... | [-0.010637825354933739, 0.049490492790937424, 0.0008666878566145897, -0.026396922767162323, -0.011116912588477135, -0.02983156032860279, 0.04066409170627594, -0.006100289989262819, 0.005309219937771559, 0.017921870574355125, 0.020653370767831802, -0.02095170132815838, -0.013956001959741116, 0.06987880170345306, -0.0026... |
40,433 | 40,433 | ['Jaydip Sen', 'P. Balamuralidhar', 'M. Girish Chandra', 'Harihara S. G.', 'Harish Reddy'] | 1012.2514v1 | In a dynamic heterogeneous environment, such as pervasive and ubiquitous
computing, context-aware adaptation is a key concept to meet the varying
requirements of different users. Connectivity is an important context source
that can be utilized for optimal management of diverse networking resources.
Application QoS (Qua... | Context Aware End-to-End Connectivity Management | 2,010 | http://arxiv.org/pdf/1012.2514v1 | Title Context Aware EndtoEnd Connectivity Management Summary dynamic heterogeneous environment pervasive ubiquitous computing contextaware adaptation key concept meet varying requirement different user Connectivity important context source utilized optimal management diverse networking resource Application QoS Quality ... | [-0.011681274510920048, -0.049678653478622437, -0.03071439638733864, -0.033424459397792816, -0.019076120108366013, -0.043289314955472946, 0.001731115160509944, -0.040650781244039536, -0.05092458426952362, -0.034483667463064194, 0.0021821721456944942, 0.04263055697083473, 0.009747988544404507, 0.004107656888663769, -0.0... |
40,434 | 40,434 | ['Yuesheng Xu', 'Haizhang Zhang', 'Qinghui Zhang'] | 1102.1324v1 | This paper studies the construction of a refinement kernel for a given
operator-valued reproducing kernel such that the vector-valued reproducing
kernel Hilbert space of the refinement kernel contains that of the given one as
a subspace. The study is motivated from the need of updating the current
operator-valued repro... | Refinement of Operator-valued Reproducing Kernels | 2,011 | http://arxiv.org/pdf/1102.1324v1 | Title Refinement Operatorvalued Reproducing Kernels Summary paper study construction refinement kernel given operatorvalued reproducing kernel vectorvalued reproducing kernel Hilbert space refinement kernel contains given one subspace study motivated need updating current operatorvalued reproducing kernel multitask lea... | [-0.04058101773262024, 0.002062075072899461, -0.016445217654109, 0.02876720391213894, 0.0002765085082501173, -0.04929583892226219, 0.02022245153784752, 0.0357179120182991, -0.0421418733894825, 0.013021821156144142, -0.00048807289567776024, 0.018163098022341728, 0.02438286319375038, 0.010919943451881409, -0.000424589001... |
40,435 | 40,435 | ['Cem Tekin', 'Mingyan Liu'] | 1102.3508v1 | In this paper we study the online learning problem involving rested and
restless multiarmed bandits with multiple plays. The system consists of a
single player/user and a set of K finite-state discrete-time Markov chains
(arms) with unknown state spaces and statistics. At each time step the player
can play M arms. The ... | Online Learning of Rested and Restless Bandits | 2,011 | http://arxiv.org/pdf/1102.3508v1 | Title Online Learning Rested Restless Bandits Summary paper study online learning problem involving rested restless multiarmed bandit multiple play system consists single playeruser set K finitestate discretetime Markov chain arm unknown state space statistic time step player play arm objective user decide step K arm p... | [-0.013716498389840126, 0.014653187245130539, -0.01814441569149494, -0.06391686946153641, -0.007744941394776106, -0.00031889244564808905, -0.0026929499581456184, 0.019389919936656952, -0.0374709852039814, -0.0056071835570037365, -0.0017937240190804005, 0.016076184809207916, -0.053290970623493195, 0.006135296076536179, ... |
40,436 | 40,436 | ['Manas A. Pathak', 'Mehrbod Sharifi', 'Bhiksha Raj'] | 1102.4021v2 | Email is a private medium of communication, and the inherent privacy
constraints form a major obstacle in developing effective spam filtering
methods which require access to a large amount of email data belonging to
multiple users. To mitigate this problem, we envision a privacy preserving spam
filtering system, where ... | Privacy Preserving Spam Filtering | 2,011 | http://arxiv.org/pdf/1102.4021v2 | Title Privacy Preserving Spam Filtering Summary Email private medium communication inherent privacy constraint form major obstacle developing effective spam filtering method require access large amount email data belonging multiple user mitigate problem envision privacy preserving spam filtering system server able trai... | [0.05335427448153496, 0.043901629745960236, -0.027694644406437874, 0.0027716667391359806, -0.04806884005665779, -0.015176949091255665, 0.06387370824813843, 0.024198807775974274, 0.07549292594194412, -0.07344169914722443, 0.010013969615101814, 0.004214583430439234, -0.04396883398294449, 0.055237364023923874, -0.01993795... |
40,437 | 40,437 | ['Vincent Gripon', 'Claude Berrou'] | 1102.4240v1 | Coded recurrent neural networks with three levels of sparsity are introduced.
The first level is related to the size of messages, much smaller than the
number of available neurons. The second one is provided by a particular coding
rule, acting as a local constraint in the neural activity. The third one is a
characteris... | Sparse neural networks with large learning diversity | 2,011 | http://arxiv.org/pdf/1102.4240v1 | Title Sparse neural network large learning diversity Summary Coded recurrent neural network three level sparsity introduced first level related size message much smaller number available neuron second one provided particular coding rule acting local constraint neural activity third one characteristic low final connecti... | [-0.013131286017596722, 0.017885690554976463, 0.004463877063244581, 0.042043741792440414, 0.004247666802257299, -0.023627111688256264, 0.02238982543349266, -0.004302415531128645, -0.026891011744737625, -0.01997786946594715, 0.014922210946679115, -0.024358754977583885, 0.014609604142606258, 0.08070508390665054, 0.025863... |
40,438 | 40,438 | ['Arvind Narayanan', 'Elaine Shi', 'Benjamin I. P. Rubinstein'] | 1102.4374v1 | This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anony... | Link Prediction by De-anonymization: How We Won the Kaggle Social
Network Challenge | 2,011 | http://arxiv.org/pdf/1102.4374v1 | Title Link Prediction Deanonymization Kaggle Social Network Challenge Summary paper describes winning entry IJCNN 2011 Social Network Challenge run Kagglecom goal contest promote research realworld link prediction dataset graph obtained crawling popular Flickr social photo sharing website user identity scrubbed deanony... | [0.04381046071648598, 0.029864363372325897, -0.029891015961766243, -0.0009283635881729424, -0.02701568230986595, -0.0576777458190918, 0.024023933336138725, -0.0024084579199552536, 0.05766779184341431, -0.0030509072821587324, -0.016661521047353745, 0.05632903799414635, -0.023327475413680077, 0.05920444801449776, 0.04541... |
40,439 | 40,439 | ['Radim Řeh{ů}řek'] | 1102.5597v1 | With the explosion of the size of digital dataset, the limiting factor for
decomposition algorithms is the \emph{number of passes} over the input, as the
input is often stored out-of-core or even off-site. Moreover, we're only
interested in algorithms that operate in \emph{constant memory} w.r.t. to the
input size, so ... | Fast and Faster: A Comparison of Two Streamed Matrix Decomposition
Algorithms | 2,011 | http://arxiv.org/pdf/1102.5597v1 | Title Fast Faster Comparison Two Streamed Matrix Decomposition Algorithms Summary explosion size digital dataset limiting factor decomposition algorithm emphnumber pass input input often stored outofcore even offsite Moreover interested algorithm operate emphconstant memory wrt input size arbitrarily large input proces... | [-0.017311837524175644, 0.044995538890361786, -0.019035859033465385, 0.014180190861225128, -0.017251810058951378, 0.017915785312652588, 0.014299463480710983, 0.02958986721932888, -0.02453421615064144, -0.03218042477965355, 0.008186760358512402, -0.0033274174202233553, 0.03849092498421669, 0.02254636213183403, -0.041635... |
40,440 | 40,440 | ['Wahiba Ben Abdessalem Karaa'] | 1102.5728v1 | This paper introduces a named entity recognition approach in textual corpus.
This Named Entity (NE) can be a named: location, person, organization, date,
time, etc., characterized by instances. A NE is found in texts accompanied by
contexts: words that are left or right of the NE. The work mainly aims at
identifying co... | Named Entity Recognition Using Web Document Corpus | 2,011 | http://arxiv.org/pdf/1102.5728v1 | Title Named Entity Recognition Using Web Document Corpus Summary paper introduces named entity recognition approach textual corpus Named Entity NE named location person organization date time etc characterized instance NE found text accompanied context word left right NE work mainly aim identifying context inducing NEs... | [0.050518445670604706, 0.0003095581487286836, 0.018921896815299988, 0.0272502563893795, -0.001999913016334176, 0.06367451697587967, 0.007857482880353928, 0.0531846359372139, 0.0032762589398771524, -0.08147557824850082, -6.393183866748586e-05, -0.01582382805645466, 0.017247553914785385, -0.02485865168273449, -0.01239887... |
40,441 | 40,441 | ['Jeff M. Phillips', 'Suresh Venkatasubramanian'] | 1103.1625v2 | This document reviews the definition of the kernel distance, providing a
gentle introduction tailored to a reader with background in theoretical
computer science, but limited exposure to technology more common to machine
learning, functional analysis and geometric measure theory. The key aspect of
the kernel distance d... | A Gentle Introduction to the Kernel Distance | 2,011 | http://arxiv.org/pdf/1103.1625v2 | Title Gentle Introduction Kernel Distance Summary document review definition kernel distance providing gentle introduction tailored reader background theoretical computer science limited exposure technology common machine learning functional analysis geometric measure theory key aspect kernel distance developed interpr... | [-0.03430366516113281, -0.03003401681780815, -0.05438952520489693, 0.04043497517704964, -0.06585151702165604, -0.03915788605809212, 0.014056208543479443, 0.02273094281554222, -0.02181917615234852, 0.01391929667443037, 0.08696269243955612, 0.013857759535312653, 0.03587630018591881, 0.028803743422031403, -0.0310881864279... |
40,442 | 40,442 | ['Hua Ouyang', 'Alexander Gray'] | 1105.2274v1 | In this paper, we focus on the question of the extent to which online
learning can benefit from distributed computing. We focus on the setting in
which $N$ agents online-learn cooperatively, where each agent only has access
to its own data. We propose a generic data-distributed online learning
meta-algorithm. We then i... | Data-Distributed Weighted Majority and Online Mirror Descent | 2,011 | http://arxiv.org/pdf/1105.2274v1 | Title DataDistributed Weighted Majority Online Mirror Descent Summary paper focus question extent online learning benefit distributed computing focus setting N agent onlinelearn cooperatively agent access data propose generic datadistributed online learning metaalgorithm introduce Distributed Weighted Majority Distribu... | [-0.008095704019069672, 0.06287980079650879, -0.009347891435027122, -0.05815209075808525, -0.05492109805345535, -0.009143348783254623, 0.06093887984752655, -0.018909255042672157, -0.011472145095467567, 0.010743960738182068, 0.007794508710503578, 0.005796857178211212, 0.00866268016397953, -0.0007949781138449907, -0.0356... |
40,443 | 40,443 | ['Nathan Keller', 'Elchanan Mossel', 'Tomer Schlank'] | 1105.2651v1 | The entropy/influence conjecture, raised by Friedgut and Kalai in 1996, seeks
to relate two different measures of concentration of the Fourier coefficients
of a Boolean function. Roughly saying, it claims that if the Fourier spectrum
is "smeared out", then the Fourier coefficients are concentrated on "high"
levels. In ... | A Note on the Entropy/Influence Conjecture | 2,011 | http://arxiv.org/pdf/1105.2651v1 | Title Note EntropyInfluence Conjecture Summary entropyinfluence conjecture raised Friedgut Kalai 1996 seek relate two different measure concentration Fourier coefficient Boolean function Roughly saying claim Fourier spectrum smeared Fourier coefficient concentrated high level note generalize conjecture biased product m... | [-0.046705618500709534, 0.00589234521612525, -0.022418469190597534, 0.023377349600195885, -0.058923956006765366, 0.0031981344800442457, -0.03570364788174629, -0.01303965412080288, -0.06407658010721207, -0.011812582612037659, -0.00047706643817946315, -0.018081791698932648, 0.032433804124593735, -0.04607446491718292, 0.0... |
40,444 | 40,444 | ['Alejandro Chinea', 'Elka Korutcheva'] | 1106.1113v1 | Graph-based representations of images have recently acquired an important
role for classification purposes within the context of machine learning
approaches. The underlying idea is to consider that relevant information of an
image is implicitly encoded into the relationships between more basic entities
that compose by ... | Complexity Analysis of Vario-eta through Structure | 2,011 | http://arxiv.org/pdf/1106.1113v1 | Title Complexity Analysis Varioeta Structure Summary Graphbased representation image recently acquired important role classification purpose within context machine learning approach underlying idea consider relevant information image implicitly encoded relationship basic entity compose whole image classification proble... | [0.028029773384332657, -0.026818692684173584, -0.03118187189102173, 0.036237932741642, -0.009286381304264069, -0.013351447880268097, -0.005831494461745024, 0.004818853922188282, 0.017185529693961143, -0.027568114921450615, 0.013751523569226265, -0.040592897683382034, 0.039839111268520355, 0.07224604487419128, 0.0141162... |
40,445 | 40,445 | ['Andrei N. Soklakov'] | 1106.2882v1 | The recent crisis and the following flight to simplicity put most derivative
businesses around the world under considerable pressure. We argue that the
traditional modeling techniques must be extended to include product design. We
propose a quantitative framework for creating products which meet the challenge
of being ... | Learning, investments and derivatives | 2,011 | http://arxiv.org/pdf/1106.2882v1 | Title Learning investment derivative Summary recent crisis following flight simplicity put derivative business around world considerable pressure argue traditional modeling technique must extended include product design propose quantitative framework creating product meet challenge optimal investor point view remaining... | [-0.012964962981641293, 0.0032524282578378916, -0.04890253767371178, -0.015313949435949326, 0.017314137890934944, -0.03863007202744484, 0.08063368499279022, -0.0018333394546061754, -0.04829415678977966, 0.015777284279465675, 0.03724389895796776, 0.022425612434744835, -0.02029533125460148, 0.1180749461054802, 0.01286941... |
40,446 | 40,446 | ['Sławomir Staworko', 'Piotr Wieczorek'] | 1106.3725v3 | We investigate the problem of learning XML queries, path queries and tree
pattern queries, from examples given by the user. A learning algorithm takes on
the input a set of XML documents with nodes annotated by the user and returns a
query that selects the nodes in a manner consistent with the annotation. We
study two ... | Learning XML Twig Queries | 2,011 | http://arxiv.org/pdf/1106.3725v3 | Title Learning XML Twig Queries Summary investigate problem learning XML query path query tree pattern query example given user learning algorithm take input set XML document node annotated user return query selects node manner consistent annotation study two learning setting differ type annotation first setting user m... | [0.01732443831861019, -0.018138404935598373, -0.04111219942569733, 0.01595362462103367, -0.0036186419893056154, -0.01312639657407999, -0.03088042326271534, 0.04128484055399895, 0.020431367680430412, -0.020378833636641502, 0.0020067139994353056, 0.08088325709104538, -0.028626717627048492, 0.040031857788562775, -0.020722... |
40,447 | 40,447 | ['Haizhang Zhang', 'Liang Zhao'] | 1106.4075v1 | To help understand various reproducing kernels used in applied sciences, we
investigate the inclusion relation of two reproducing kernel Hilbert spaces.
Characterizations in terms of feature maps of the corresponding reproducing
kernels are established. A full table of inclusion relations among widely-used
translation ... | On the Inclusion Relation of Reproducing Kernel Hilbert Spaces | 2,011 | http://arxiv.org/pdf/1106.4075v1 | Title Inclusion Relation Reproducing Kernel Hilbert Spaces Summary help understand various reproducing kernel used applied science investigate inclusion relation two reproducing kernel Hilbert space Characterizations term feature map corresponding reproducing kernel established full table inclusion relation among widel... | [-0.04156538471579552, -0.021605517715215683, -0.004156978335231543, 0.00632587680593133, -0.021801991388201714, -0.007710620760917664, -0.004362447187304497, 0.022654244676232338, 0.01049487479031086, 0.016329044476151466, -0.031856928020715714, 0.003924938850104809, 0.033610958606004715, 0.00492897117510438, 0.020578... |
40,448 | 40,448 | ['Feng Niu', 'Benjamin Recht', 'Christopher Re', 'Stephen J. Wright'] | 1106.5730v2 | Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve
state-of-the-art performance on a variety of machine learning tasks. Several
researchers have recently proposed schemes to parallelize SGD, but all require
performance-destroying memory locking and synchronization. This work aims to
show using no... | HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient
Descent | 2,011 | http://arxiv.org/pdf/1106.5730v2 | Title HOGWILD LockFree Approach Parallelizing Stochastic Gradient Descent Summary Stochastic Gradient Descent SGD popular algorithm achieve stateoftheart performance variety machine learning task Several researcher recently proposed scheme parallelize SGD require performancedestroying memory locking synchronization wor... | [0.004885129630565643, 0.03907415643334389, 0.010512537322938442, 0.013273569755256176, -0.0060016317293047905, -0.007038085255771875, 0.04037092998623848, -0.018938684836030006, 0.00581173924729228, -0.006551637779921293, -0.002931873081251979, -0.0016088885022327304, 0.010615813545882702, -0.03279566392302513, -0.041... |
40,449 | 40,449 | ['Jeff M. Phillips', 'Parasaran Raman', 'Suresh Venkatasubramanian'] | 1108.0017v1 | We provide a new framework for generating multiple good quality partitions
(clusterings) of a single data set. Our approach decomposes this problem into
two components, generating many high-quality partitions, and then grouping
these partitions to obtain k representatives. The decomposition makes the
approach extremely... | Generating a Diverse Set of High-Quality Clusterings | 2,011 | http://arxiv.org/pdf/1108.0017v1 | Title Generating Diverse Set HighQuality Clusterings Summary provide new framework generating multiple good quality partition clustering single data set approach decomposes problem two component generating many highquality partition grouping partition obtain k representative decomposition make approach extremely modula... | [-0.04027153551578522, 0.022773627191781998, -0.05941138416528702, -0.004752946086227894, -0.00579395005479455, -0.012312346138060093, 0.0947207659482956, 0.036089882254600525, 0.050908591598272324, -0.012774871662259102, 0.011398497968912125, 0.03726387023925781, 0.0017199504654854536, 0.053205907344818115, 0.01884794... |
40,450 | 40,450 | ['Yao Wu', 'Qiang Yan', 'Danny Bickson', 'Yucheng Low', 'Qing Yang'] | 1108.2580v2 | This paper describes the solution method taken by LeBuSiShu team for track1
in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two
main challenges: the unique item taxonomy characteristics as well as the large
data set size.To handle the item taxonomy, we present a novel method called
Matrix Factor... | Efficient Multicore Collaborative Filtering | 2,011 | http://arxiv.org/pdf/1108.2580v2 | Title Efficient Multicore Collaborative Filtering Summary paper describes solution method taken LeBuSiShu team track1 ACM KDD CUP 2011 contest resulting 5th place identified two main challenge unique item taxonomy characteristic well large data set sizeTo handle item taxonomy present novel method called Matrix Factoriz... | [-0.005671095103025436, -0.017328254878520966, -0.007633903529495001, 0.00183896126691252, -0.023127306252717972, -0.006768211256712675, 0.015174571424722672, 0.03263797610998154, 0.0047952583990991116, -0.008574698120355606, -0.05167130008339882, 0.034689418971538544, -0.019638169556856155, 0.03711261972784996, -0.062... |
40,451 | 40,451 | ['Meir Perez', 'Tshilidzi Marwala'] | 1108.4545v1 | The Fuzzy Gene Filter (FGF) is an optimised Fuzzy Inference System designed
to rank genes in order of differential expression, based on expression data
generated in a microarray experiment. This paper examines the effectiveness of
the FGF for feature selection using various classification architectures. The
FGF is comp... | The fuzzy gene filter: A classifier performance assesment | 2,011 | http://arxiv.org/pdf/1108.4545v1 | Title fuzzy gene filter classifier performance assesment Summary Fuzzy Gene Filter FGF optimised Fuzzy Inference System designed rank gene order differential expression based expression data generated microarray experiment paper examines effectiveness FGF feature selection using various classification architecture FGF ... | [0.010194633156061172, 0.015802031382918358, -0.05856885015964508, -0.026595301926136017, 0.009030790999531746, 0.02499782107770443, 0.07045989483594894, 0.05544121563434601, 0.044243600219488144, -0.0030834400095045567, 0.04584519937634468, 0.009285980835556984, 0.02706695720553398, 0.0508841909468174, -0.026519143953... |
40,452 | 40,452 | ['Mlungisi Duma', 'Bhekisipho Twala', 'Tshilidzi Marwala'] | 1108.4551v1 | The Ripper algorithm is designed to generate rule sets for large datasets
with many features. However, it was shown that the algorithm struggles with
classification performance in the presence of missing data. The algorithm
struggles to classify instances when the quality of the data deteriorates as a
result of increas... | Improving the performance of the ripper in insurance risk classification
: A comparitive study using feature selection | 2,011 | http://arxiv.org/pdf/1108.4551v1 | Title Improving performance ripper insurance risk classification comparitive study using feature selection Summary Ripper algorithm designed generate rule set large datasets many feature However shown algorithm struggle classification performance presence missing data algorithm struggle classify instance quality data d... | [-0.03227360174059868, 0.03989262506365776, -0.04628752917051315, -0.005425445735454559, 0.0038489813450723886, 0.027814051136374474, 0.021330172196030617, 0.05551028251647949, -0.017009466886520386, -0.00890099536627531, 0.06992381066083908, 0.0352860689163208, 0.022255616262555122, 0.06659016758203506, -0.01257159281... |
40,453 | 40,453 | ['Massimo Melucci'] | 1108.5784v1 | The Probability Ranking Principle states that the document set with the
highest values of probability of relevance optimizes information retrieval
effectiveness given the probabilities are estimated as accurately as possible.
The key point of the principle is the separation of the document set into two
subsets with a g... | Probability Ranking in Vector Spaces | 2,011 | http://arxiv.org/pdf/1108.5784v1 | Title Probability Ranking Vector Spaces Summary Probability Ranking Principle state document set highest value probability relevance optimizes information retrieval effectiveness given probability estimated accurately possible key point principle separation document set two subset given level fallout highest recall pap... | [0.03646327182650566, 0.022608602419495583, -0.024488143622875214, 0.010572039522230625, -0.021908624097704887, 0.006696054246276617, 0.03295242413878441, 0.014115915633738041, -0.024763092398643494, -0.051713407039642334, 0.027809174731373787, -0.006469155661761761, 0.003717937972396612, 0.010345447808504105, 0.002115... |
40,454 | 40,454 | ['Dean Foster', 'Alexander Rakhlin'] | 1108.6088v1 | We present an algorithm which attains O(\sqrt{T}) internal (and thus
external) regret for finite games with partial monitoring under the local
observability condition. Recently, this condition has been shown by (Bartok,
Pal, and Szepesvari, 2011) to imply the O(\sqrt{T}) rate for partial monitoring
games against an i.i... | No Internal Regret via Neighborhood Watch | 2,011 | http://arxiv.org/pdf/1108.6088v1 | Title Internal Regret via Neighborhood Watch Summary present algorithm attains OsqrtT internal thus external regret finite game partial monitoring local observability condition Recently condition shown Bartok Pal Szepesvari 2011 imply OsqrtT rate partial monitoring game iid opponent author conjectured hold nonstochasti... | [-0.028885263949632645, 0.07380794733762741, -0.0077223023399710655, -0.03094932809472084, -0.058986663818359375, -0.02986287698149681, -0.03293204307556152, 0.0199408121407032, 0.005857918411493301, 0.024358395487070084, 0.024003084748983383, 0.023645352572202682, -0.06771283596754074, 0.007228503935039043, 0.00605176... |
40,455 | 40,455 | ['Zenglin Xu', 'Feng Yan', 'Yuan', 'Qi'] | 1108.6296v2 | Tensor decomposition is a powerful computational tool for multiway data
analysis. Many popular tensor decomposition approaches---such as the Tucker
decomposition and CANDECOMP/PARAFAC (CP)---amount to multi-linear
factorization. They are insufficient to model (i) complex interactions between
data entities, (ii) various... | Infinite Tucker Decomposition: Nonparametric Bayesian Models for
Multiway Data Analysis | 2,011 | http://arxiv.org/pdf/1108.6296v2 | Title Infinite Tucker Decomposition Nonparametric Bayesian Models Multiway Data Analysis Summary Tensor decomposition powerful computational tool multiway data analysis Many popular tensor decomposition approachessuch Tucker decomposition CANDECOMPPARAFAC CPamount multilinear factorization insufficient model complex in... | [-0.0397573858499527, 0.052809204906225204, -0.021833444014191628, -0.0011870188172906637, -0.0009496218990534544, 0.03206805884838104, 0.04665861278772354, 0.03684527426958084, -0.018377449363470078, 0.021731767803430557, 0.017620794475078583, 0.01850310154259205, -0.0054091508500278, 0.039467811584472656, -0.00530939... |
40,456 | 40,456 | ['Matus Telgarsky'] | 1110.1514v1 | This manuscript investigates the relationship between Blackwell
Approachability, a stochastic vector-valued repeated game, and minimax theory,
a single-play scalar-valued scenario. First, it is established in a general
setting --- one not permitting invocation of minimax theory --- that
Blackwell's Approachability Theo... | Blackwell Approachability and Minimax Theory | 2,011 | http://arxiv.org/pdf/1110.1514v1 | Title Blackwell Approachability Minimax Theory Summary manuscript investigates relationship Blackwell Approachability stochastic vectorvalued repeated game minimax theory singleplay scalarvalued scenario First established general setting one permitting invocation minimax theory Blackwells Approachability Theorem genera... | [0.007566201500594616, -0.05488952621817589, -0.034085098654031754, 0.008108546026051044, -0.021745888516306877, -0.06042355298995972, -0.023612461984157562, 0.002361014485359192, -0.03276069462299347, 0.045144904404878616, -0.008495624177157879, 0.004608249291777611, -0.023165876045823097, 0.005720503628253937, 0.0049... |
40,457 | 40,457 | ['G. Kesidis', 'A. Kurve'] | 1110.1781v1 | We consider unsupervised crowdsourcing performance based on the model wherein
the responses of end-users are essentially rated according to how their
responses correlate with the majority of other responses to the same
subtasks/questions. In one setting, we consider an independent sequence of
identically distributed cr... | A Study of Unsupervised Adaptive Crowdsourcing | 2,011 | http://arxiv.org/pdf/1110.1781v1 | Title Study Unsupervised Adaptive Crowdsourcing Summary consider unsupervised crowdsourcing performance based model wherein response endusers essentially rated according response correlate majority response subtasksquestions one setting consider independent sequence identically distributed crowdsourcing assignment meta... | [0.04418741539120674, 0.009188026189804077, -0.009902602061629295, -0.010587126016616821, -0.023117342963814735, -0.01513640396296978, -0.013867486268281937, -0.01949048973619938, -0.04021793231368065, -0.028012383729219437, -0.03026706725358963, 0.021158087998628616, -0.00368309672921896, 0.003373813582584262, -0.0059... |
40,458 | 40,458 | ['Dip Narayan Ray', 'Somajyoti Majumder', 'Sumit Mukhopadhyay'] | 1110.1796v1 | The use of mobile robots is being popular over the world mainly for
autonomous explorations in hazardous/ toxic or unknown environments. This
exploration will be more effective and efficient if the explorations in unknown
environment can be aided with the learning from past experiences. Currently
reinforcement learning... | A Behavior-based Approach for Multi-agent Q-learning for Autonomous
Exploration | 2,011 | http://arxiv.org/pdf/1110.1796v1 | Title Behaviorbased Approach Multiagent Qlearning Autonomous Exploration Summary use mobile robot popular world mainly autonomous exploration hazardous toxic unknown environment exploration effective efficient exploration unknown environment aided learning past experience Currently reinforcement learning getting accept... | [0.021604781970381737, -0.03661254793405533, -0.009501338005065918, -0.04273391142487526, 0.01019483245909214, -0.014268930070102215, 0.019427388906478882, -0.040378108620643616, -0.02276538498699665, -0.0389939621090889, -0.04908838868141174, 0.06408891081809998, -0.04603172466158867, 0.016105175018310547, -0.02001627... |
40,459 | 40,459 | ['Ohad Shamir'] | 1110.2392v2 | We provide a variant of Azuma's concentration inequality for martingales, in
which the standard boundedness requirement is replaced by the milder
requirement of a subgaussian tail. | A Variant of Azuma's Inequality for Martingales with Subgaussian Tails | 2,011 | http://arxiv.org/pdf/1110.2392v2 | Title Variant Azumas Inequality Martingales Subgaussian Tails Summary provide variant Azumas concentration inequality martingale standard boundedness requirement replaced milder requirement subgaussian tail Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian Vlad Serban Tim Klinger Gerald Te... | [-0.009964226745069027, -0.02787928096950054, -0.042300958186388016, -0.01910429075360298, 0.0029517330694943666, -0.0035435666795819998, -0.029368741437792778, -0.0008533383370377123, -0.00923298578709364, 0.061040546745061874, 0.062027379870414734, 0.002602418651804328, -0.04415658116340637, -0.016748568043112755, -0... |
40,460 | 40,460 | ['Parul Agarwal', 'M. Afshar Alam', 'Ranjit Biswas'] | 1110.2610v1 | Clustering is an unsupervised technique of Data Mining. It means grouping
similar objects together and separating the dissimilar ones. Each object in the
data set is assigned a class label in the clustering process using a distance
measure. This paper has captured the problems that are faced in real when
clustering alg... | Issues,Challenges and Tools of Clustering Algorithms | 2,011 | http://arxiv.org/pdf/1110.2610v1 | Title IssuesChallenges Tools Clustering Algorithms Summary Clustering unsupervised technique Data Mining mean grouping similar object together separating dissimilar one object data set assigned class label clustering process using distance measure paper captured problem faced real clustering algorithm implemented also ... | [0.00741969607770443, 0.009911803528666496, -0.029504045844078064, 0.058433692902326584, -0.004695613868534565, -0.023943843320012093, 0.06507731229066849, 0.025506513193249702, -0.013212619349360466, -0.0013171392492949963, 0.02764797769486904, 0.053061287850141525, 0.045003343373537064, 0.006497976370155811, -0.04481... |
40,461 | 40,461 | ['K. Usha Rani'] | 1110.2626v1 | One of the important techniques of Data mining is Classification. Many real
world problems in various fields such as business, science, industry and
medicine can be solved by using classification approach. Neural Networks have
emerged as an important tool for classification. The advantages of Neural
Networks helps for ... | Analysis of Heart Diseases Dataset using Neural Network Approach | 2,011 | http://arxiv.org/pdf/1110.2626v1 | Title Analysis Heart Diseases Dataset using Neural Network Approach Summary One important technique Data mining Classification Many real world problem various field business science industry medicine solved using classification approach Neural Networks emerged important tool classification advantage Neural Networks hel... | [-0.009211324155330658, 0.024765290319919586, -0.03653160482645035, 0.009831558912992477, 0.02508270926773548, 0.025086427107453346, 0.028974195942282677, 0.017673401162028313, 0.010109558701515198, 0.039405789226293564, 0.03780662268400192, 0.030956100672483444, 0.051442716270685196, 0.025810671970248222, -0.013694418... |
40,462 | 40,462 | ['Daniel Hsu', 'Sham M. Kakade', 'Tong Zhang'] | 1110.2842v1 | We prove an exponential probability tail inequality for positive semidefinite
quadratic forms in a subgaussian random vector. The bound is analogous to one
that holds when the vector has independent Gaussian entries. | A tail inequality for quadratic forms of subgaussian random vectors | 2,011 | http://arxiv.org/pdf/1110.2842v1 | Title tail inequality quadratic form subgaussian random vector Summary prove exponential probability tail inequality positive semidefinite quadratic form subgaussian random vector bound analogous one hold vector independent Gaussian entry Authors 0 Ahmed Osman Wojciech Samek 1 Ji Young Lee Franck Dernoncourt 2 Iulian V... | [-0.013714881613850594, 0.0008419348741881549, -0.024875205010175705, 0.03204478323459625, -0.016443030908703804, 0.0143289091065526, -0.025625670328736305, -0.006597098428755999, -0.01632525771856308, -0.015618914738297462, 0.03477989137172699, -0.006898859981447458, -0.05313068628311157, 0.029160695150494576, -0.0024... |
40,463 | 40,463 | ['Christos Boutsidis', 'Anastasios Zouzias', 'Michael W. Mahoney', 'Petros Drineas'] | 1110.2897v3 | We study the topic of dimensionality reduction for $k$-means clustering.
Dimensionality reduction encompasses the union of two approaches: \emph{feature
selection} and \emph{feature extraction}. A feature selection based algorithm
for $k$-means clustering selects a small subset of the input features and then
applies $k... | Randomized Dimensionality Reduction for k-means Clustering | 2,011 | http://arxiv.org/pdf/1110.2897v3 | Title Randomized Dimensionality Reduction kmeans Clustering Summary study topic dimensionality reduction kmeans clustering Dimensionality reduction encompasses union two approach emphfeature selection emphfeature extraction feature selection based algorithm kmeans clustering selects small subset input feature applies k... | [-0.036905281245708466, -0.036915361881256104, -0.05960271880030632, 0.029135333374142647, 0.02626664564013481, -0.02281659096479416, 0.05244864150881767, 0.018098406493663788, -0.0009620037162676454, 0.02119303122162819, 0.05181598290801048, 0.039168838411569595, 0.022225718945264816, 0.04023393988609314, -0.028229072... |
40,464 | 40,464 | ['Peng Cheng'] | 1110.3001v1 | We propose a first-order method for stochastic strongly convex optimization
that attains $O(1/n)$ rate of convergence, analysis show that the proposed
method is simple, easily to implement, and in worst case, asymptotically four
times faster than its peers. We derive this method from several intuitive
observations that... | Step size adaptation in first-order method for stochastic strongly
convex programming | 2,011 | http://arxiv.org/pdf/1110.3001v1 | Title Step size adaptation firstorder method stochastic strongly convex programming Summary propose firstorder method stochastic strongly convex optimization attains O1n rate convergence analysis show proposed method simple easily implement worst case asymptotically four time faster peer derive method several intuitive... | [-0.007402130868285894, 0.011893377639353275, 0.003955082967877388, -0.0035117112565785646, 0.007753078360110521, -0.01196298934519291, -0.02796700969338417, -0.00035457091871649027, -0.01854410581290722, 0.01386135071516037, 0.011233935132622719, 0.011598821729421616, -0.00897132232785225, 0.09838902950286865, -0.0072... |
40,465 | 40,465 | ['C. Staiger', 'S. Cadot', 'R. Kooter', 'M. Dittrich', 'T. Mueller', 'G. W. Klau', 'L. F. A. Wessels'] | 1110.3717v2 | Recently, several classifiers that combine primary tumor data, like gene
expression data, and secondary data sources, such as protein-protein
interaction networks, have been proposed for predicting outcome in breast
cancer. In these approaches, new composite features are typically constructed
by aggregating the express... | A critical evaluation of network and pathway based classifiers for
outcome prediction in breast cancer | 2,011 | http://arxiv.org/pdf/1110.3717v2 | Title critical evaluation network pathway based classifier outcome prediction breast cancer Summary Recently several classifier combine primary tumor data like gene expression data secondary data source proteinprotein interaction network proposed predicting outcome breast cancer approach new composite feature typically... | [0.006087668240070343, 0.06257890164852142, -0.03029298409819603, -0.06149847432971001, -0.009559647180140018, 0.019785230979323387, 0.053882647305727005, 0.010427337139844894, -0.00768444687128067, 0.023588895797729492, 0.038237668573856354, -0.03276168182492256, 0.013506680727005005, 0.090369813144207, -0.02916058339... |
40,466 | 40,466 | ['Wouter Lueks', 'Bassam Mokbel', 'Michael Biehl', 'Barbara Hammer'] | 1110.3917v1 | The growing number of dimensionality reduction methods available for data
visualization has recently inspired the development of quality assessment
measures, in order to evaluate the resulting low-dimensional representation
independently from a methods' inherent criteria. Several (existing) quality
measures can be (re)... | How to Evaluate Dimensionality Reduction? - Improving the Co-ranking
Matrix | 2,011 | http://arxiv.org/pdf/1110.3917v1 | Title Evaluate Dimensionality Reduction Improving Coranking Matrix Summary growing number dimensionality reduction method available data visualization recently inspired development quality assessment measure order evaluate resulting lowdimensional representation independently method inherent criterion Several existing ... | [-0.05682862922549248, -0.015106872655451298, -0.030593179166316986, 0.032980695366859436, -0.009262307547032833, -0.006148871034383774, 0.06567328423261642, 0.03204140067100525, -0.009414350613951683, 0.02742868848145008, -0.015505214221775532, 0.0197173859924078, 0.04507710039615631, 0.035846419632434845, -0.00662798... |
40,467 | 40,467 | ['Georgios C. Chasparis', 'Ari Arapostathis', 'Jeff S. Shamma'] | 1110.4412v1 | We consider the problem of distributed convergence to efficient outcomes in
coordination games through dynamics based on aspiration learning. Under
aspiration learning, a player continues to play an action as long as the
rewards received exceed a specified aspiration level. Here, the aspiration
level is a fading memory... | Aspiration Learning in Coordination Games | 2,011 | http://arxiv.org/pdf/1110.4412v1 | Title Aspiration Learning Coordination Games Summary consider problem distributed convergence efficient outcome coordination game dynamic based aspiration learning aspiration learning player continues play action long reward received exceed specified aspiration level aspiration level fading memory average past reward l... | [0.0005067908787168562, 0.025769149884581566, -0.015248771756887436, -0.03698379173874855, -0.03039039857685566, -0.025763902813196182, -0.04938212409615517, -0.01985963061451912, -0.0334230400621891, 0.02858392708003521, -0.000359184603439644, 0.033625341951847076, -0.020903032273054123, 0.021530935540795326, -0.02582... |
40,468 | 40,468 | ['Sébastien Bubeck', 'Damien Ernst', 'Aurélien Garivier'] | 1110.5447v1 | We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on the
optimistic paradigm and the Good-Turing missing mass estimator. We show that
this strategy uniformly attains... | Optimal discovery with probabilistic expert advice | 2,011 | http://arxiv.org/pdf/1110.5447v1 | Title Optimal discovery probabilistic expert advice Summary consider original problem arises issue security analysis power system name optimal discovery probabilistic expert advice address algorithm based optimistic paradigm GoodTuring missing mass estimator show strategy uniformly attains optimal discovery rate macros... | [-0.0012803737772628665, 0.04294147342443466, -0.00937445554882288, -0.007802258245646954, -0.014699519611895084, -0.0787624940276146, 0.01362521480768919, -0.026773786172270775, -0.025512948632240295, 0.03810250386595726, 0.09093555808067322, 0.01744815520942211, -0.028730474412441254, 0.050946786999702454, 0.01390909... |
40,469 | 40,469 | ['Sanjeev Arora', 'Rong Ge', 'Ravi Kannan', 'Ankur Moitra'] | 1111.0952v1 | In the Nonnegative Matrix Factorization (NMF) problem we are given an $n
\times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express
$M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$
and $r \times m$ respectively. In some applications, it makes sense to ask
instead for the... | Computing a Nonnegative Matrix Factorization -- Provably | 2,011 | http://arxiv.org/pdf/1111.0952v1 | Title Computing Nonnegative Matrix Factorization Provably Summary Nonnegative Matrix Factorization NMF problem given n time nonnegative matrix integer r 0 goal express W W nonnegative matrix size n time r r time respectively application make sense ask instead product AW approximate ie approximately minimize normM AWF n... | [-0.08133860677480698, 0.02188250981271267, 0.0048426054418087006, 0.03408069908618927, -0.02228177711367607, -0.027285335585474968, -0.01738961972296238, 0.0029941389802843332, -0.029346348717808723, 0.005450672935694456, -0.00837821327149868, 0.009839549660682678, -0.01139398105442524, 0.04886729270219803, -0.0035888... |
40,470 | 40,470 | ['Lisa Hellerstein', 'Devorah Kletenik', 'Linda Sellie', 'Rocco Servedio'] | 1111.1124v1 | We prove a new structural lemma for partial Boolean functions $f$, which we
call the seed lemma for DNF. Using the lemma, we give the first subexponential
algorithm for proper learning of DNF in Angluin's Equivalence Query (EQ) model.
The algorithm has time and query complexity $2^{(\tilde{O}{\sqrt{n}})}$, which
is opt... | Tight Bounds on Proper Equivalence Query Learning of DNF | 2,011 | http://arxiv.org/pdf/1111.1124v1 | Title Tight Bounds Proper Equivalence Query Learning DNF Summary prove new structural lemma partial Boolean function f call seed lemma DNF Using lemma give first subexponential algorithm proper learning DNF Angluins Equivalence Query EQ model algorithm time query complexity 2tildeOsqrtn optimal also give new result cer... | [-0.021013574674725533, 0.05939974635839462, -0.024251198396086693, -0.0017215233528986573, -0.03751815855503082, -0.017360946163535118, -0.002020846586674452, 0.025274382904171944, 0.007154291495680809, -0.013926906511187553, 0.025458354502916336, 0.06036722660064697, -0.013162296265363693, 0.06681164354085922, -0.007... |
40,471 | 40,471 | ['Yuyang Wang', 'Roni Khardon', 'Pavlos Protopapas'] | 1111.1315v2 | Many real world problems exhibit patterns that have periodic behavior. For
example, in astrophysics, periodic variable stars play a pivotal role in
understanding our universe. An important step when analyzing data from such
processes is the problem of identifying the period: estimating the period of a
periodic function... | Nonparametric Bayesian Estimation of Periodic Functions | 2,011 | http://arxiv.org/pdf/1111.1315v2 | Title Nonparametric Bayesian Estimation Periodic Functions Summary Many real world problem exhibit pattern periodic behavior example astrophysics periodic variable star play pivotal role understanding universe important step analyzing data process problem identifying period estimating period periodic function based noi... | [-0.020405437797307968, 0.044719673693180084, -0.021982142701745033, 0.022782498970627785, 0.012237870134413242, -0.0130709707736969, 0.022722819820046425, 0.035347580909729004, -0.0914686769247055, 0.05058387294411659, 0.019915280863642693, -0.005873583722859621, 0.030791016295552254, 0.03179618716239929, -0.021457318... |
40,472 | 40,472 | ['Shipra Agrawal', 'Navin Goyal'] | 1111.1797v3 | The multi-armed bandit problem is a popular model for studying
exploration/exploitation trade-off in sequential decision problems. Many
algorithms are now available for this well-studied problem. One of the earliest
algorithms, given by W. R. Thompson, dates back to 1933. This algorithm,
referred to as Thompson Samplin... | Analysis of Thompson Sampling for the multi-armed bandit problem | 2,011 | http://arxiv.org/pdf/1111.1797v3 | Title Analysis Thompson Sampling multiarmed bandit problem Summary multiarmed bandit problem popular model studying explorationexploitation tradeoff sequential decision problem Many algorithm available wellstudied problem One earliest algorithm given W R Thompson date back 1933 algorithm referred Thompson Sampling natu... | [0.007474137935787439, 0.02580147422850132, -0.01992236077785492, -0.05787891894578934, 0.0033037387765944004, -0.024110887199640274, -0.014801020734012127, 0.01576211117208004, -0.00411974499002099, -0.022831320762634277, -0.01940758526325226, 0.027255989611148834, -0.04420897737145424, -0.015092381276190281, 0.011187... |
40,473 | 40,473 | ['Sanmay Das', 'Allen Lavoie', 'Malik Magdon-Ismail'] | 1111.2092v1 | As a major source for information on virtually any topic, Wikipedia serves an
important role in public dissemination and consumption of knowledge. As a
result, it presents tremendous potential for people to promulgate their own
points of view; such efforts may be more subtle than typical vandalism. In this
paper, we in... | Pushing Your Point of View: Behavioral Measures of Manipulation in
Wikipedia | 2,011 | http://arxiv.org/pdf/1111.2092v1 | Title Pushing Point View Behavioral Measures Manipulation Wikipedia Summary major source information virtually topic Wikipedia serf important role public dissemination consumption knowledge result present tremendous potential people promulgate point view effort may subtle typical vandalism paper introduce new behaviora... | [0.0835721492767334, 0.055036064237356186, -0.002005439018830657, -0.006926395930349827, -0.01183839701116085, -0.022137992084026337, 0.015819290652871132, -0.01462238933891058, -0.020081324502825737, -0.03359947353601456, 0.05719330906867981, 0.023280905559659004, -0.012879989109933376, 0.0589054636657238, -0.02030234... |
40,474 | 40,474 | ['Song Liu', 'Peter Flach', 'Nello Cristianini'] | 1111.2111v2 | In this paper we introduce a generic model for multiplicative algorithms
which is suitable for the MapReduce parallel programming paradigm. We implement
three typical machine learning algorithms to demonstrate how similarity
comparison, gradient descent, power method and other classic learning
techniques fit this model... | Generic Multiplicative Methods for Implementing Machine Learning
Algorithms on MapReduce | 2,011 | http://arxiv.org/pdf/1111.2111v2 | Title Generic Multiplicative Methods Implementing Machine Learning Algorithms MapReduce Summary paper introduce generic model multiplicative algorithm suitable MapReduce parallel programming paradigm implement three typical machine learning algorithm demonstrate similarity comparison gradient descent power method class... | [-0.0046873618848621845, 0.027820125222206116, -0.01428820751607418, -0.024819057434797287, -0.04636220633983612, 0.02041865885257721, 0.03429890796542168, 0.022974791005253792, -0.026705268770456314, -0.025074755772948265, 0.03262483701109886, -0.002980815712362528, 0.017595242708921432, -0.008348620496690273, 0.00209... |
40,475 | 40,475 | ['Rong Jin', 'Tianbao Yang', 'Mehrdad Mahdavi', 'Yu-Feng Li', 'Zhi-Hua Zhou'] | 1111.2262v4 | We develop two approaches for analyzing the approximation error bound for the
Nystr\"{o}m method, one based on the concentration inequality of integral
operator, and one based on the compressive sensing theory. We show that the
approximation error, measured in the spectral norm, can be improved from
$O(N/\sqrt{m})$ to ... | Improved Bound for the Nystrom's Method and its Application to Kernel
Classification | 2,011 | http://arxiv.org/pdf/1111.2262v4 | Title Improved Bound Nystroms Method Application Kernel Classification Summary develop two approach analyzing approximation error bound Nystrom method one based concentration inequality integral operator one based compressive sensing theory show approximation error measured spectral norm improved ONsqrtm ONm1 rho case ... | [-0.004557634238153696, -0.021713348105549812, -0.03685520961880684, 0.03963432461023331, 0.034161582589149475, -0.026348356157541275, -0.02824351377785206, 0.05017203837633133, 0.0051629734225571156, 0.04059290885925293, 0.0036451066844165325, 0.014908810146152973, 0.050394147634506226, -0.06617944687604904, -0.049263... |
40,476 | 40,476 | ['Jacob Abernethy', 'Rafael M. Frongillo'] | 1111.2664v1 | Machine Learning competitions such as the Netflix Prize have proven
reasonably successful as a method of "crowdsourcing" prediction tasks. But
these competitions have a number of weaknesses, particularly in the incentive
structure they create for the participants. We propose a new approach, called a
Crowdsourced Learni... | A Collaborative Mechanism for Crowdsourcing Prediction Problems | 2,011 | http://arxiv.org/pdf/1111.2664v1 | Title Collaborative Mechanism Crowdsourcing Prediction Problems Summary Machine Learning competition Netflix Prize proven reasonably successful method crowdsourcing prediction task competition number weakness particularly incentive structure create participant propose new approach called Crowdsourced Learning Mechanism... | [0.043289538472890854, 0.0932631865143776, -0.028352640569210052, -0.0008137922268360853, -0.038014695048332214, -0.037009093910455704, 0.03944040462374687, 0.03198350965976715, -0.030697038397192955, -0.025985516607761383, 0.004614692181348801, 0.045650187879800797, -0.0033454925287514925, 0.06984755396842957, 0.00437... |
40,477 | 40,477 | ['Marcos A. Domingues', 'Alipio Mario Jorge', 'Carlos Soares'] | 1111.2948v2 | Traditionally, recommender systems for the Web deal with applications that
have two dimensions, users and items. Based on access logs that relate these
dimensions, a recommendation model can be built and used to identify a set of N
items that will be of interest to a certain user. In this paper we propose a
method to c... | Using Contextual Information as Virtual Items on Top-N Recommender
Systems | 2,011 | http://arxiv.org/pdf/1111.2948v2 | Title Using Contextual Information Virtual Items TopN Recommender Systems Summary Traditionally recommender system Web deal application two dimension user item Based access log relate dimension recommendation model built used identify set N item interest certain user paper propose method complement information access l... | [0.0010286709293723106, -0.018283464014530182, -0.012580196373164654, 0.01220325194299221, -0.0020166137255728245, -0.019559934735298157, 0.07376115024089813, 0.0025184904225170612, -9.555765427649021e-05, -0.03892987594008446, -0.055147312581539154, 0.01899905502796173, -0.004921970423310995, 0.07588223367929459, -0.0... |
40,478 | 40,478 | ['Matthew D. Hoffman', 'Andrew Gelman'] | 1111.4246v1 | Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm
that avoids the random walk behavior and sensitivity to correlated parameters
that plague many MCMC methods by taking a series of steps informed by
first-order gradient information. These features allow it to converge to
high-dimensional targe... | The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian
Monte Carlo | 2,011 | http://arxiv.org/pdf/1111.4246v1 | Title NoUTurn Sampler Adaptively Setting Path Lengths Hamiltonian Monte Carlo Summary Hamiltonian Monte Carlo HMC Markov chain Monte Carlo MCMC algorithm avoids random walk behavior sensitivity correlated parameter plague many MCMC method taking series step informed firstorder gradient information feature allow converg... | [-0.04490730166435242, -0.017258888110518456, 0.0009946783538907766, -0.04158544912934303, -0.008480435237288475, -0.059921395033597946, 0.02442995458841324, -0.0035952189937233925, -0.0454888753592968, 0.012552128173410892, 0.06712477654218674, 0.08803404122591019, 0.014949130825698376, 0.007245113141834736, 0.0088000... |
40,479 | 40,479 | ['Francis Bach'] | 1111.6453v2 | Submodular functions are relevant to machine learning for at least two
reasons: (1) some problems may be expressed directly as the optimization of
submodular functions and (2) the lovasz extension of submodular functions
provides a useful set of regularization functions for supervised and
unsupervised learning. In this... | Learning with Submodular Functions: A Convex Optimization Perspective | 2,011 | http://arxiv.org/pdf/1111.6453v2 | Title Learning Submodular Functions Convex Optimization Perspective Summary Submodular function relevant machine learning least two reason 1 problem may expressed directly optimization submodular function 2 lovasz extension submodular function provides useful set regularization function supervised unsupervised learning... | [-0.03289031237363815, 0.013274774886667728, -0.03151078149676323, 0.01190526969730854, -0.013063324615359306, -0.03605254366993904, 0.0652390867471695, 0.03747071325778961, -0.005954527296125889, -0.025248924270272255, -7.607141742482781e-05, 0.010013964958488941, 0.018591832369565964, 0.01616767980158329, 0.018294421... |
40,480 | 40,480 | ['Raajay Viswanathan', 'Prateek Jain', 'Srivatsan Laxman', 'Arvind Arasu'] | 1111.7295v2 | In this paper, we consider the problem of estimating self-tuning histograms
using query workloads. To this end, we propose a general learning theoretic
formulation. Specifically, we use query feedback from a workload as training
data to estimate a histogram with a small memory footprint that minimizes the
expected erro... | A Learning Framework for Self-Tuning Histograms | 2,011 | http://arxiv.org/pdf/1111.7295v2 | Title Learning Framework SelfTuning Histograms Summary paper consider problem estimating selftuning histogram using query workload end propose general learning theoretic formulation Specifically use query feedback workload training data estimate histogram small memory footprint minimizes expected error future query for... | [-0.060072608292102814, 0.00954117625951767, -0.018759265542030334, -0.00530221126973629, -0.005847308784723282, -0.016385110095143318, 0.04299316927790642, 0.016327660530805588, 0.04959607869386673, 0.05925130099058151, -0.004683283623307943, 0.03687116503715515, -0.040968190878629684, 0.030345970764756203, -0.0075696... |
40,481 | 40,481 | ['Fabian Pedregosa', 'Gaël Varoquaux', 'Alexandre Gramfort', 'Vincent Michel', 'Bertrand Thirion', 'Olivier Grisel', 'Mathieu Blondel', 'Gilles Louppe', 'Peter Prettenhofer', 'Ron Weiss', 'Vincent Dubourg', 'Jake Vanderplas', 'Alexandre Passos', 'David Cournapeau', 'Matthieu Brucher', 'Matthieu Perrot', 'Édouard Duches... | 1201.0490v3 | Scikit-learn is a Python module integrating a wide range of state-of-the-art
machine learning algorithms for medium-scale supervised and unsupervised
problems. This package focuses on bringing machine learning to non-specialists
using a general-purpose high-level language. Emphasis is put on ease of use,
performance, d... | Scikit-learn: Machine Learning in Python | 2,012 | http://arxiv.org/pdf/1201.0490v3 | Title Scikitlearn Machine Learning Python Summary Scikitlearn Python module integrating wide range stateoftheart machine learning algorithm mediumscale supervised unsupervised problem package focus bringing machine learning nonspecialists using generalpurpose highlevel language Emphasis put ease use performance documen... | [-0.023782163858413696, -0.019803276285529137, -0.007959798909723759, -0.011520509608089924, 0.006755550857633352, 0.0013267978793010116, 0.045777782797813416, 0.022119978442788124, 0.036771342158317566, -0.030418872833251953, 0.02885068580508232, 0.03819590061903, 0.017087725922465324, 0.09669168293476105, -0.00578111... |
40,482 | 40,482 | ['Sung-eok Jeon', 'Chuanyi Ji'] | 1201.2575v2 | For a large multi-hop wireless network, nodes are preferable to make
distributed and localized link-scheduling decisions with only interactions
among a small number of neighbors. However, for a slowly decaying channel and
densely populated interferers, a small size neighborhood often results in
nontrivial link outages ... | Joint Approximation of Information and Distributed Link-Scheduling
Decisions in Wireless Networks | 2,012 | http://arxiv.org/pdf/1201.2575v2 | Title Joint Approximation Information Distributed LinkScheduling Decisions Wireless Networks Summary large multihop wireless network node preferable make distributed localized linkscheduling decision interaction among small number neighbor However slowly decaying channel densely populated interferers small size neighbo... | [-0.03622050955891609, -0.023025421425700188, -0.012278659269213676, -0.03993459790945053, -0.03050478734076023, -0.03134356066584587, 0.033758778125047684, -0.030161961913108826, -0.005623499862849712, -0.008753341622650623, -0.005552194081246853, 0.04719796031713486, -0.003879763185977936, -0.030918510630726814, -0.0... |
40,483 | 40,483 | ['Geetha Manjunatha', 'M Narasimha Murty', 'Dinkar Sitaram'] | 1201.2925v2 | Most enterprise data is distributed in multiple relational databases with
expert-designed schema. Using traditional single-table machine learning
techniques over such data not only incur a computational penalty for converting
to a 'flat' form (mega-join), even the human-specified semantic information
present in the rel... | Combining Heterogeneous Classifiers for Relational Databases | 2,012 | http://arxiv.org/pdf/1201.2925v2 | Title Combining Heterogeneous Classifiers Relational Databases Summary enterprise data distributed multiple relational database expertdesigned schema Using traditional singletable machine learning technique data incur computational penalty converting flat form megajoin even humanspecified semantic information present r... | [-0.0019899792969226837, 0.01491063367575407, -0.004826454911381006, -0.011555309407413006, 0.005849676672369242, 0.043990496546030045, 0.013232766650617123, 0.004107834305614233, -0.011569815687835217, -0.055312033742666245, -0.016532178968191147, -0.0019815561827272177, 0.00527906883507967, 0.015601527877151966, -0.0... |
40,484 | 40,484 | ['Keqin Liu', 'Qing Zhao'] | 1201.4906v1 | We consider the adaptive shortest-path routing problem in wireless networks
under unknown and stochastically varying link states. In this problem, we aim
to optimize the quality of communication between a source and a destination
through adaptive path selection. Due to the randomness and uncertainties in the
network dy... | Adaptive Shortest-Path Routing under Unknown and Stochastically Varying
Link States | 2,012 | http://arxiv.org/pdf/1201.4906v1 | Title Adaptive ShortestPath Routing Unknown Stochastically Varying Link States Summary consider adaptive shortestpath routing problem wireless network unknown stochastically varying link state problem aim optimize quality communication source destination adaptive path selection Due randomness uncertainty network dynami... | [-0.017567873001098633, -0.00883940514177084, -0.0022445530630648136, -0.09108451008796692, -0.02464054711163044, -0.07272612303495407, -0.03142378106713295, -0.0438542403280735, -0.0033798597287386656, -0.009286333806812763, 0.01535288617014885, 0.039543796330690384, -0.002583449939265847, -0.025008201599121094, 0.026... |
40,485 | 40,485 | ['Bo Zhao', 'Benjamin I. P. Rubinstein', 'Jim Gemmell', 'Jiawei Han'] | 1203.0058v1 | In practical data integration systems, it is common for the data sources
being integrated to provide conflicting information about the same entity.
Consequently, a major challenge for data integration is to derive the most
complete and accurate integrated records from diverse and sometimes conflicting
sources. We term ... | A Bayesian Approach to Discovering Truth from Conflicting Sources for
Data Integration | 2,012 | http://arxiv.org/pdf/1203.0058v1 | Title Bayesian Approach Discovering Truth Conflicting Sources Data Integration Summary practical data integration system common data source integrated provide conflicting information entity Consequently major challenge data integration derive complete accurate integrated record diverse sometimes conflicting source term... | [0.010385017842054367, 0.08955126255750656, -0.018067579716444016, 0.01762313023209572, -0.07671617716550827, 0.0025337664410471916, -0.0023278426378965378, 0.0038639591075479984, -0.014699184335768223, -0.015661459416151047, 0.06227758154273033, 0.042597174644470215, 0.06489681452512741, 0.06181884929537773, -0.054552... |
40,486 | 40,486 | ['T Soni Madhulatha'] | 1203.2002v1 | Clustering is a common technique for statistical data analysis, Clustering is
the process of grouping the data into classes or clusters so that objects
within a cluster have high similarity in comparison to one another, but are
very dissimilar to objects in other clusters. Dissimilarities are assessed
based on the attr... | Graph partitioning advance clustering technique | 2,012 | http://arxiv.org/pdf/1203.2002v1 | Title Graph partitioning advance clustering technique Summary Clustering common technique statistical data analysis Clustering process grouping data class cluster object within cluster high similarity comparison one another dissimilar object cluster Dissimilarities assessed based attribute value describing object Often... | [-0.03013598546385765, -0.03673066943883896, -0.04833868145942688, 0.07500111311674118, -0.005695030093193054, -0.03104904107749462, 0.08227904886007309, 0.03879469260573387, 0.018108583986759186, -0.010135019198060036, 0.008239655755460262, 0.03880618140101433, 0.009322159923613071, 0.015360410325229168, -0.0137226730... |
40,487 | 40,487 | ['Surjeet Kumar Yadav', 'Brijesh Bharadwaj', 'Saurabh Pal'] | 1203.2987v1 | The main objective of higher education is to provide quality education to
students. One way to achieve highest level of quality in higher education
system is by discovering knowledge for prediction regarding enrolment of
students in a course. This paper presents a data mining project to generate
predictive models for s... | Mining Education Data to Predict Student's Retention: A comparative
Study | 2,012 | http://arxiv.org/pdf/1203.2987v1 | Title Mining Education Data Predict Students Retention comparative Study Summary main objective higher education provide quality education student One way achieve highest level quality higher education system discovering knowledge prediction regarding enrolment student course paper present data mining project generate ... | [0.04067019000649452, -0.014662496745586395, -0.04953932762145996, -0.01762125827372074, 0.00537005253136158, 0.025555957108736038, -0.00847665686160326, -0.02127707004547119, 0.04650519788265228, -0.03643127903342247, 0.06167490407824516, 0.042757999151945114, 0.013764122501015663, 0.06032932549715042, -0.037990868091... |
40,488 | 40,488 | ['Hussein Saad', 'Amr Mohamed', 'Tamer ElBatt'] | 1203.3935v1 | In this paper, we propose a distributed reinforcement learning (RL) technique
called distributed power control using Q-learning (DPC-Q) to manage the
interference caused by the femtocells on macro-users in the downlink. The DPC-Q
leverages Q-Learning to identify the sub-optimal pattern of power allocation,
which strive... | Distributed Cooperative Q-learning for Power Allocation in Cognitive
Femtocell Networks | 2,012 | http://arxiv.org/pdf/1203.3935v1 | Title Distributed Cooperative Qlearning Power Allocation Cognitive Femtocell Networks Summary paper propose distributed reinforcement learning RL technique called distributed power control using Qlearning DPCQ manage interference caused femtocells macrousers downlink DPCQ leverage QLearning identify suboptimal pattern ... | [-0.008876192383468151, 0.003917040769010782, -0.02563466690480709, -0.051042042672634125, -0.02085263654589653, -0.02443547733128071, 0.027497487142682076, -0.027508916333317757, -0.03425954282283783, 0.02560112252831459, -0.04615703225135803, 0.013955884613096714, -0.003834603587165475, -0.03349985182285309, -0.01880... |
40,489 | 40,489 | ['Rajiv Khanna', 'Liang Zhang', 'Deepak Agarwal', 'Beechung Chen'] | 1203.5124v1 | Predicting user affinity to items is an important problem in applications
like content optimization, computational advertising, and many more. While
bilinear random effect models (matrix factorization) provide state-of-the-art
performance when minimizing RMSE through a Gaussian response model on explicit
ratings data, ... | Parallel Matrix Factorization for Binary Response | 2,012 | http://arxiv.org/pdf/1203.5124v1 | Title Parallel Matrix Factorization Binary Response Summary Predicting user affinity item important problem application like content optimization computational advertising many bilinear random effect model matrix factorization provide stateoftheart performance minimizing RMSE Gaussian response model explicit rating dat... | [0.016388026997447014, 0.002745093312114477, -0.041088495403528214, -0.006879848428070545, 0.019185442477464676, 0.028349382802844048, 0.02986254170536995, 0.029029332101345062, 0.03333953768014908, -0.043502893298864365, -0.06168684735894203, -0.022908983752131462, -0.025021854788064957, 0.05306939035654068, -0.025397... |
40,490 | 40,490 | ['Philippe Lauret', 'Auline Rodler', 'Marc Muselli', 'Mathieu David', 'Hadja Diagne', 'Cyril Voyant'] | 1203.5446v1 | This paper proposes to use a rather new modelling approach in the realm of
solar radiation forecasting. In this work, two forecasting models:
Autoregressive Moving Average (ARMA) and Neural Network (NN) models are
combined to form a model committee. The Bayesian inference is used to affect a
probability to each model i... | A Bayesian Model Committee Approach to Forecasting Global Solar
Radiation | 2,012 | http://arxiv.org/pdf/1203.5446v1 | Title Bayesian Model Committee Approach Forecasting Global Solar Radiation Summary paper proposes use rather new modelling approach realm solar radiation forecasting work two forecasting model Autoregressive Moving Average ARMA Neural Network NN model combined form model committee Bayesian inference used affect probabi... | [0.02921825461089611, 0.02299194410443306, -0.00038427949766628444, -0.04153311625123024, -0.011121361516416073, 0.021429704502224922, 0.031844835728406906, -0.02691463939845562, 0.057028718292713165, 0.008513273671269417, 0.029924195259809494, 0.060781676322221756, 0.055157966911792755, 0.019630858674645424, 0.0230339... |
40,491 | 40,491 | ['Elad Hazan', 'Satyen Kale', 'Shai Shalev-Shwartz'] | 1204.0136v1 | In several online prediction problems of recent interest the comparison class
is composed of matrices with bounded entries. For example, in the online
max-cut problem, the comparison class is matrices which represent cuts of a
given graph and in online gambling the comparison class is matrices which
represent permutati... | Near-Optimal Algorithms for Online Matrix Prediction | 2,012 | http://arxiv.org/pdf/1204.0136v1 | Title NearOptimal Algorithms Online Matrix Prediction Summary several online prediction problem recent interest comparison class composed matrix bounded entry example online maxcut problem comparison class matrix represent cut given graph online gambling comparison class matrix represent permutation n team Another impo... | [-0.0083452258259058, 0.026189863681793213, -0.019127516075968742, -0.03993656486272812, -0.029751183465123177, -0.004520578775554895, 0.02971576526761055, 0.061239466071128845, 0.006080298218876123, 0.009584516286849976, -0.003417797153815627, -0.009761142544448376, -0.016216784715652466, 0.1017545685172081, -0.003915... |
40,492 | 40,492 | ['Jia Zeng', 'Zhi-Qiang Liu', 'Xiao-Qin Cao'] | 1204.0170v2 | Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic
modeling paradigm, and recently finds many applications in computer vision and
computational biology. In this paper, we propose a fast and accurate batch
algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA
algorithms requ... | A New Approach to Speeding Up Topic Modeling | 2,012 | http://arxiv.org/pdf/1204.0170v2 | Title New Approach Speeding Topic Modeling Summary Latent Dirichlet allocation LDA widelyused probabilistic topic modeling paradigm recently find many application computer vision computational biology paper propose fast accurate batch algorithm active belief propagation ABP training LDA Usually batch LDA algorithm requ... | [0.045277900993824005, 0.01594267413020134, -0.0077589647844433784, 0.005606784950941801, -0.024153636768460274, 0.015051943250000477, 0.002328617963939905, 0.034713223576545715, -0.031079573556780815, -0.05897429212927818, 0.015014669857919216, -0.0005522504216060042, 0.001299244468100369, 0.046202536672353745, -0.006... |
40,493 | 40,493 | ['Ali Soltan Mohammadi', 'L. Asadzadeh', 'D. D. Rezaee'] | 1204.1467v1 | The rough-set theory proposed by Pawlak, has been widely used in dealing with
data classification problems. The original rough-set model is, however, quite
sensitive to noisy data. Tzung thus proposed deals with the problem of
producing a set of fuzzy certain and fuzzy possible rules from quantitative
data with a prede... | Learning Fuzzy β-Certain and β-Possible rules from
incomplete quantitative data by rough sets | 2,012 | http://arxiv.org/pdf/1204.1467v1 | Title Learning Fuzzy βCertain βPossible rule incomplete quantitative data rough set Summary roughset theory proposed Pawlak widely used dealing data classification problem original roughset model however quite sensitive noisy data Tzung thus proposed deal problem producing set fuzzy certain fuzzy possible rule quantita... | [-0.008604594506323338, 0.04992089793086052, -0.03573612868785858, 0.005504218395799398, 0.0520445816218853, 0.00623143557459116, -0.02128736302256584, 0.0543900802731514, -0.060818158090114594, -0.016900120303034782, 0.055998582392930984, -0.04552559554576874, 0.03377960994839668, 0.032802995294332504, -0.024321582168... |
40,494 | 40,494 | ['Paulo F. C. Tilles', 'Jose F. Fontanari'] | 1204.1564v4 | An explanation for the acquisition of word-object mappings is the associative
learning in a cross-situational scenario. Here we present analytical results of
the performance of a simple associative learning algorithm for acquiring a
one-to-one mapping between $N$ objects and $N$ words based solely on the
co-occurrence ... | Minimal model of associative learning for cross-situational lexicon
acquisition | 2,012 | http://arxiv.org/pdf/1204.1564v4 | Title Minimal model associative learning crosssituational lexicon acquisition Summary explanation acquisition wordobject mapping associative learning crosssituational scenario present analytical result performance simple associative learning algorithm acquiring onetoone mapping N object N word based solely cooccurrence... | [0.028988344594836235, 0.006461146287620068, -0.0003566641826182604, 0.08050159364938736, -0.033397313207387924, 0.016601983457803726, 0.0038417575415223837, 0.020140618085861206, 0.003987549804151058, -0.048212360590696335, -0.03716030344367027, -0.034772176295518875, 0.033422909677028656, 0.012743574567139149, -0.005... |
40,495 | 40,495 | ['Shalini Puri', 'Sona Kaushik'] | 1204.2058v1 | In this new and current era of technology, advancements and techniques,
efficient and effective text document classification is becoming a challenging
and highly required area to capably categorize text documents into mutually
exclusive categories. Fuzzy similarity provides a way to find the similarity of
features amon... | A technical study and analysis on fuzzy similarity based models for text
classification | 2,012 | http://arxiv.org/pdf/1204.2058v1 | Title technical study analysis fuzzy similarity based model text classification Summary new current era technology advancement technique efficient effective text document classification becoming challenging highly required area capably categorize text document mutually exclusive category Fuzzy similarity provides way f... | [0.04494453966617584, -0.014866065233945847, -0.031974103301763535, 0.01804627664387226, -0.039590466767549515, 0.03703761473298073, 0.009517819620668888, 0.05883296951651573, 0.017060106620192528, -0.10225218534469604, -0.00307719805277884, 0.006457677576690912, 0.01104025263339281, -0.027271069586277008, -0.038901373... |
40,496 | 40,496 | ['Shalini Puri'] | 1204.2061v1 | Text Classification is a challenging and a red hot field in the current
scenario and has great importance in text categorization applications. A lot of
research work has been done in this field but there is a need to categorize a
collection of text documents into mutually exclusive categories by extracting
the concepts... | A Fuzzy Similarity Based Concept Mining Model for Text Classification | 2,012 | http://arxiv.org/pdf/1204.2061v1 | Title Fuzzy Similarity Based Concept Mining Model Text Classification Summary Text Classification challenging red hot field current scenario great importance text categorization application lot research work done field need categorize collection text document mutually exclusive category extracting concept feature using... | [0.04370729625225067, 0.002435473958030343, -0.04265874996781349, 0.03567979484796524, -0.02158309519290924, 0.02992284670472145, -0.007792997173964977, 0.0372229665517807, -0.06161671504378319, -0.08113507181406021, -0.0008127085166051984, -0.0007746033370494843, 0.02052106335759163, 0.009924427606165409, -0.033610865... |
40,497 | 40,497 | ['Valentina Fedorova', 'Alex Gammerman', 'Ilia Nouretdinov', 'Vladimir Vovk'] | 1204.3251v2 | A standard assumption in machine learning is the exchangeability of data,
which is equivalent to assuming that the examples are generated from the same
probability distribution independently. This paper is devoted to testing the
assumption of exchangeability on-line: the examples arrive one by one, and
after receiving ... | Plug-in martingales for testing exchangeability on-line | 2,012 | http://arxiv.org/pdf/1204.3251v2 | Title Plugin martingale testing exchangeability online Summary standard assumption machine learning exchangeability data equivalent assuming example generated probability distribution independently paper devoted testing assumption exchangeability online example arrive one one receiving example would like valid measure ... | [0.006676100194454193, 0.04274365305900574, -0.013664867728948593, 0.012571236118674278, -0.035303745418787, -0.007819239050149918, -0.00829676166176796, -0.009020675905048847, 0.01355015765875578, 0.014451977796852589, 0.06789512187242508, 0.028345778584480286, 0.019585028290748596, 0.09383845329284668, -0.03941883891... |
40,498 | 40,498 | ['Maria-Florina Balcan', 'Avrim Blum', 'Shai Fine', 'Yishay Mansour'] | 1204.3514v3 | We consider the problem of PAC-learning from distributed data and analyze
fundamental communication complexity questions involved. We provide general
upper and lower bounds on the amount of communication needed to learn well,
showing that in addition to VC-dimension and covering number, quantities such
as the teaching-... | Distributed Learning, Communication Complexity and Privacy | 2,012 | http://arxiv.org/pdf/1204.3514v3 | Title Distributed Learning Communication Complexity Privacy Summary consider problem PAClearning distributed data analyze fundamental communication complexity question involved provide general upper lower bound amount communication needed learn well showing addition VCdimension covering number quantity teachingdimensio... | [-0.00014148751506581903, 0.04299638420343399, -0.013660618104040623, 0.02393127977848053, -0.040874503552913666, -0.022397814318537712, 0.060144875198602676, -0.05679234489798546, 0.006550255697220564, 0.006440427154302597, -0.02484811097383499, 0.020564937964081764, 0.010421697050333023, 0.030369697138667107, -0.0047... |
40,499 | 40,499 | ['Seyda Ertekin', 'Haym Hirsh', 'Cynthia Rudin'] | 1204.3611v1 | The problem of "approximating the crowd" is that of estimating the crowd's
majority opinion by querying only a subset of it. Algorithms that approximate
the crowd can intelligently stretch a limited budget for a crowdsourcing task.
We present an algorithm, "CrowdSense," that works in an online fashion to
dynamically sa... | Learning to Predict the Wisdom of Crowds | 2,012 | http://arxiv.org/pdf/1204.3611v1 | Title Learning Predict Wisdom Crowds Summary problem approximating crowd estimating crowd majority opinion querying subset Algorithms approximate crowd intelligently stretch limited budget crowdsourcing task present algorithm CrowdSense work online fashion dynamically sample subset labelers based explorationexploitatio... | [0.05981838330626488, 0.0718386247754097, -0.02103772573173046, 0.00718695530667901, 0.00020797421166207641, -0.03053440898656845, -0.03037877194583416, 0.015119343996047974, -0.03035658784210682, -0.014003682881593704, 0.0013191935140639544, 0.013439824804663658, 0.006744399666786194, 0.05514524132013321, 0.0158067196... |
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