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32,302 | Logistic Regression: Tight Bounds for Stochastic and Online Optimization | cs.LG | The logistic loss function is often advocated in machine learning and
statistics as a smooth and strictly convex surrogate for the 0-1 loss. In this
paper we investigate the question of whether these smoothness and convexity
properties make the logistic loss preferable to other widely considered options
such as the hin... | computer science |
32,303 | A two-step learning approach for solving full and almost full cold start
problems in dyadic prediction | cs.LG | Dyadic prediction methods operate on pairs of objects (dyads), aiming to
infer labels for out-of-sample dyads. We consider the full and almost full cold
start problem in dyadic prediction, a setting that occurs when both objects in
an out-of-sample dyad have not been observed during training, or if one of them
has been... | computer science |
32,304 | Online Learning with Composite Loss Functions | cs.LG | We study a new class of online learning problems where each of the online
algorithm's actions is assigned an adversarial value, and the loss of the
algorithm at each step is a known and deterministic function of the values
assigned to its recent actions. This class includes problems where the
algorithm's loss is the mi... | computer science |
32,305 | A Distributed Algorithm for Training Nonlinear Kernel Machines | cs.LG | This paper concerns the distributed training of nonlinear kernel machines on
Map-Reduce. We show that a re-formulation of Nystr\"om approximation based
solution which is solved using gradient based techniques is well suited for
this, especially when it is necessary to work with a large number of basis
points. The main ... | computer science |
32,306 | A distributed block coordinate descent method for training $l_1$
regularized linear classifiers | cs.LG | Distributed training of $l_1$ regularized classifiers has received great
attention recently. Most existing methods approach this problem by taking steps
obtained from approximating the objective by a quadratic approximation that is
decoupled at the individual variable level. These methods are designed for
multicore and... | computer science |
32,307 | Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms | cs.LG | We consider stochastic multi-armed bandit problems where the expected reward
is a Lipschitz function of the arm, and where the set of arms is either
discrete or continuous. For discrete Lipschitz bandits, we derive asymptotic
problem specific lower bounds for the regret satisfied by any algorithm, and
propose OSLB and ... | computer science |
32,308 | Online Linear Optimization via Smoothing | cs.LG | We present a new optimization-theoretic approach to analyzing
Follow-the-Leader style algorithms, particularly in the setting where
perturbations are used as a tool for regularization. We show that adding a
strongly convex penalty function to the decision rule and adding stochastic
perturbations to data correspond to d... | computer science |
32,309 | Visualizing Random Forest with Self-Organising Map | cs.LG | Random Forest (RF) is a powerful ensemble method for classification and
regression tasks. It consists of decision trees set. Although, a single tree is
well interpretable for human, the ensemble of trees is a black-box model. The
popular technique to look inside the RF model is to visualize a RF proximity
matrix obtain... | computer science |
32,310 | Proximal Reinforcement Learning: A New Theory of Sequential Decision
Making in Primal-Dual Spaces | cs.LG | In this paper, we set forth a new vision of reinforcement learning developed
by us over the past few years, one that yields mathematically rigorous
solutions to longstanding important questions that have remained unresolved:
(i) how to design reliable, convergent, and robust reinforcement learning
algorithms (ii) how t... | computer science |
32,311 | BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization,
Experimental Design and Bandits | cs.LG | BayesOpt is a library with state-of-the-art Bayesian optimization methods to
solve nonlinear optimization, stochastic bandits or sequential experimental
design problems. Bayesian optimization is sample efficient by building a
posterior distribution to capture the evidence and prior knowledge for the
target function. Bu... | computer science |
32,312 | Effect of Different Distance Measures on the Performance of K-Means
Algorithm: An Experimental Study in Matlab | cs.LG | K-means algorithm is a very popular clustering algorithm which is famous for
its simplicity. Distance measure plays a very important rule on the performance
of this algorithm. We have different distance measure techniques available. But
choosing a proper technique for distance calculation is totally dependent on
the ty... | computer science |
32,313 | Simultaneous Feature and Expert Selection within Mixture of Experts | cs.LG | A useful strategy to deal with complex classification scenarios is the
"divide and conquer" approach. The mixture of experts (MOE) technique makes use
of this strategy by joinly training a set of classifiers, or experts, that are
specialized in different regions of the input space. A global model, or gate
function, com... | computer science |
32,314 | Flip-Flop Sublinear Models for Graphs: Proof of Theorem 1 | cs.LG | We prove that there is no class-dual for almost all sublinear models on
graphs. | computer science |
32,315 | On the Consistency of Ordinal Regression Methods | cs.LG | Many of the ordinal regression models that have been proposed in the
literature can be seen as methods that minimize a convex surrogate of the
zero-one, absolute, or squared loss functions. A key property that allows to
study the statistical implications of such approximations is that of Fisher
consistency. Fisher cons... | computer science |
32,316 | On the Complexity of Bandit Linear Optimization | cs.LG | We study the attainable regret for online linear optimization problems with
bandit feedback, where unlike the full-information setting, the player can only
observe its own loss rather than the full loss vector. We show that the price
of bandit information in this setting can be as large as $d$, disproving the
well-know... | computer science |
32,317 | Learning a hyperplane classifier by minimizing an exact bound on the VC
dimension | cs.LG | The VC dimension measures the capacity of a learning machine, and a low VC
dimension leads to good generalization. While SVMs produce state-of-the-art
learning performance, it is well known that the VC dimension of a SVM can be
unbounded; despite good results in practice, there is no guarantee of good
generalization. I... | computer science |
32,318 | Robust OS-ELM with a novel selective ensemble based on particle swarm
optimization | cs.LG | In this paper, a robust online sequential extreme learning machine (ROS-ELM)
is proposed. It is based on the original OS-ELM with an adaptive selective
ensemble framework. Two novel insights are proposed in this paper. First, a
novel selective ensemble algorithm referred to as particle swarm optimization
selective ense... | computer science |
32,319 | Linear Contour Learning: A Method for Supervised Dimension Reduction | cs.LG | We propose a novel approach to sufficient dimension reduction in regression,
based on estimating contour directions of negligible variation for the response
surface. These directions span the orthogonal complement of the minimal space
relevant for the regression, and can be extracted according to a measure of the
varia... | computer science |
32,320 | Multi-Sensor Event Detection using Shape Histograms | cs.LG | Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterize... | computer science |
32,321 | AFP Algorithm and a Canonical Normal Form for Horn Formulas | cs.LG | AFP Algorithm is a learning algorithm for Horn formulas. We show that it does
not improve the complexity of AFP Algorithm, if after each negative
counterexample more that just one refinements are performed. Moreover, a
canonical normal form for Horn formulas is presented, and it is proved that the
output formula of AFP... | computer science |
32,322 | Conic Multi-Task Classification | cs.LG | Traditionally, Multi-task Learning (MTL) models optimize the average of
task-related objective functions, which is an intuitive approach and which we
will be referring to as Average MTL. However, a more general framework,
referred to as Conic MTL, can be formulated by considering conic combinations
of the objective fun... | computer science |
32,323 | Improved Distributed Principal Component Analysis | cs.LG | We study the distributed computing setting in which there are multiple
servers, each holding a set of points, who wish to compute functions on the
union of their point sets. A key task in this setting is Principal Component
Analysis (PCA), in which the servers would like to compute a low dimensional
subspace capturing ... | computer science |
32,324 | Label Distribution Learning | cs.LG | Although multi-label learning can deal with many problems with label
ambiguity, it does not fit some real applications well where the overall
distribution of the importance of the labels matters. This paper proposes a
novel learning paradigm named \emph{label distribution learning} (LDL) for such
kind of applications. ... | computer science |
32,325 | Large Scale Purchase Prediction with Historical User Actions on B2C
Online Retail Platform | cs.LG | This paper describes the solution of Bazinga Team for Tmall Recommendation
Prize 2014. With real-world user action data provided by Tmall, one of the
largest B2C online retail platforms in China, this competition requires to
predict future user purchases on Tmall website. Predictions are judged on
F1Score, which consid... | computer science |
32,326 | Task-group Relatedness and Generalization Bounds for Regularized
Multi-task Learning | cs.LG | In this paper, we study the generalization performance of regularized
multi-task learning (RMTL) in a vector-valued framework, where MTL is
considered as a learning process for vector-valued functions. We are mainly
concerned with two theoretical questions: 1) under what conditions does RMTL
perform better with a small... | computer science |
32,327 | A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training
Structural SVMs with a Costly max-Oracle | cs.LG | Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image
segmentation or human pose estimation. Training SSVMs, however, is
computationally costly, because it requires repeated calls to a structured
prediction subroutine (called \emph... | computer science |
32,328 | A Batchwise Monotone Algorithm for Dictionary Learning | cs.LG | We propose a batchwise monotone algorithm for dictionary learning. Unlike the
state-of-the-art dictionary learning algorithms which impose sparsity
constraints on a sample-by-sample basis, we instead treat the samples as a
batch, and impose the sparsity constraint on the whole. The benefit of
batchwise optimization is ... | computer science |
32,329 | Lock in Feedback in Sequential Experiments | cs.LG | We often encounter situations in which an experimenter wants to find, by
sequential experimentation, $x_{max} = \arg\max_{x} f(x)$, where $f(x)$ is a
(possibly unknown) function of a well controllable variable $x$. Taking
inspiration from physics and engineering, we have designed a new method to
address this problem. I... | computer science |
32,330 | Learning Parametric-Output HMMs with Two Aliased States | cs.LG | In various applications involving hidden Markov models (HMMs), some of the
hidden states are aliased, having identical output distributions. The
minimality, identifiability and learnability of such aliased HMMs have been
long standing problems, with only partial solutions provided thus far. In this
paper we focus on pa... | computer science |
32,331 | SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization | cs.LG | We propose a new algorithm for minimizing regularized empirical loss:
Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each
iteration we update a random subset of the dual variables. However, unlike
existing methods such as stochastic dual coordinate ascent, SDNA is capable of
utilizing all curvat... | computer science |
32,332 | Rademacher Observations, Private Data, and Boosting | cs.LG | The minimization of the logistic loss is a popular approach to batch
supervised learning. Our paper starts from the surprising observation that,
when fitting linear (or kernelized) classifiers, the minimization of the
logistic loss is \textit{equivalent} to the minimization of an exponential
\textit{rado}-loss computed... | computer science |
32,333 | An Infinite Restricted Boltzmann Machine | cs.LG | We present a mathematical construction for the restricted Boltzmann machine
(RBM) that doesn't require specifying the number of hidden units. In fact, the
hidden layer size is adaptive and can grow during training. This is obtained by
first extending the RBM to be sensitive to the ordering of its hidden units.
Then, th... | computer science |
32,334 | Adaptive Random SubSpace Learning (RSSL) Algorithm for Prediction | cs.LG | We present a novel adaptive random subspace learning algorithm (RSSL) for
prediction purpose. This new framework is flexible where it can be adapted with
any learning technique. In this paper, we tested the algorithm for regression
and classification problems. In addition, we provide a variety of weighting
schemes to i... | computer science |
32,335 | Optimal and Adaptive Algorithms for Online Boosting | cs.LG | We study online boosting, the task of converting any weak online learner into
a strong online learner. Based on a novel and natural definition of weak online
learnability, we develop two online boosting algorithms. The first algorithm is
an online version of boost-by-majority. By proving a matching lower bound, we
show... | computer science |
32,336 | Learning Reductions that Really Work | cs.LG | We provide a summary of the mathematical and computational techniques that
have enabled learning reductions to effectively address a wide class of
problems, and show that this approach to solving machine learning problems can
be broadly useful. | computer science |
32,337 | Scalable Multilabel Prediction via Randomized Methods | cs.LG | Modeling the dependence between outputs is a fundamental challenge in
multilabel classification. In this work we show that a generic regularized
nonlinearity mapping independent predictions to joint predictions is sufficient
to achieve state-of-the-art performance on a variety of benchmark problems.
Crucially, we compu... | computer science |
32,338 | Learning Transferable Features with Deep Adaptation Networks | cs.LG | Recent studies reveal that a deep neural network can learn transferable
features which generalize well to novel tasks for domain adaptation. However,
as deep features eventually transition from general to specific along the
network, the feature transferability drops significantly in higher layers with
increasing domain... | computer science |
32,339 | Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift | cs.LG | Training Deep Neural Networks is complicated by the fact that the
distribution of each layer's inputs changes during training, as the parameters
of the previous layers change. This slows down the training by requiring lower
learning rates and careful parameter initialization, and makes it notoriously
hard to train mode... | computer science |
32,340 | Supervised LogEuclidean Metric Learning for Symmetric Positive Definite
Matrices | cs.LG | Metric learning has been shown to be highly effective to improve the
performance of nearest neighbor classification. In this paper, we address the
problem of metric learning for Symmetric Positive Definite (SPD) matrices such
as covariance matrices, which arise in many real-world applications. Naively
using standard Ma... | computer science |
32,341 | Adding vs. Averaging in Distributed Primal-Dual Optimization | cs.LG | Distributed optimization methods for large-scale machine learning suffer from
a communication bottleneck. It is difficult to reduce this bottleneck while
still efficiently and accurately aggregating partial work from different
machines. In this paper, we present a novel generalization of the recent
communication-effici... | computer science |
32,342 | Scalable Stochastic Alternating Direction Method of Multipliers | cs.LG | Stochastic alternating direction method of multipliers (ADMM), which visits
only one sample or a mini-batch of samples each time, has recently been proved
to achieve better performance than batch ADMM. However, most stochastic methods
can only achieve a convergence rate $O(1/\sqrt T)$ on general convex
problems,where T... | computer science |
32,343 | A Predictive System for detection of Bankruptcy using Machine Learning
techniques | cs.LG | Bankruptcy is a legal procedure that claims a person or organization as a
debtor. It is essential to ascertain the risk of bankruptcy at initial stages
to prevent financial losses. In this perspective, different soft computing
techniques can be employed to ascertain bankruptcy. This study proposes a
bankruptcy predicti... | computer science |
32,344 | Non-Adaptive Learning a Hidden Hipergraph | cs.LG | We give a new deterministic algorithm that non-adaptively learns a hidden
hypergraph from edge-detecting queries. All previous non-adaptive algorithms
either run in exponential time or have non-optimal query complexity. We give
the first polynomial time non-adaptive learning algorithm for learning
hypergraph that asks ... | computer science |
32,345 | Towards Biologically Plausible Deep Learning | cs.LG | Neuroscientists have long criticised deep learning algorithms as incompatible
with current knowledge of neurobiology. We explore more biologically plausible
versions of deep representation learning, focusing here mostly on unsupervised
learning but developing a learning mechanism that could account for supervised,
unsu... | computer science |
32,346 | Application of Deep Neural Network in Estimation of the Weld Bead
Parameters | cs.LG | We present a deep learning approach to estimation of the bead parameters in
welding tasks. Our model is based on a four-hidden-layer neural network
architecture. More specifically, the first three hidden layers of this
architecture utilize Sigmoid function to produce their respective intermediate
outputs. On the other ... | computer science |
32,347 | The Ladder: A Reliable Leaderboard for Machine Learning Competitions | cs.LG | The organizer of a machine learning competition faces the problem of
maintaining an accurate leaderboard that faithfully represents the quality of
the best submission of each competing team. What makes this estimation problem
particularly challenging is its sequential and adaptive nature. As participants
are allowed to... | computer science |
32,348 | Deep Transform: Error Correction via Probabilistic Re-Synthesis | cs.LG | Errors in data are usually unwelcome and so some means to correct them is
useful. However, it is difficult to define, detect or correct errors in an
unsupervised way. Here, we train a deep neural network to re-synthesize its
inputs at its output layer for a given class of data. We then exploit the fact
that this abstra... | computer science |
32,349 | Generalized Gradient Learning on Time Series under Elastic
Transformations | cs.LG | The majority of machine learning algorithms assumes that objects are
represented as vectors. But often the objects we want to learn on are more
naturally represented by other data structures such as sequences and time
series. For these representations many standard learning algorithms are
unavailable. We generalize gra... | computer science |
32,350 | Real time clustering of time series using triangular potentials | cs.LG | Motivated by the problem of computing investment portfolio weightings we
investigate various methods of clustering as alternatives to traditional
mean-variance approaches. Such methods can have significant benefits from a
practical point of view since they remove the need to invert a sample
covariance matrix, which can... | computer science |
32,351 | CSAL: Self-adaptive Labeling based Clustering Integrating Supervised
Learning on Unlabeled Data | cs.LG | Supervised classification approaches can predict labels for unknown data
because of the supervised training process. The success of classification is
heavily dependent on the labeled training data. Differently, clustering is
effective in revealing the aggregation property of unlabeled data, but the
performance of most ... | computer science |
32,352 | Supervised cross-modal factor analysis for multiple modal data
classification | cs.LG | In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared ... | computer science |
32,353 | Trust Region Policy Optimization | cs.LG | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and... | computer science |
32,354 | Achieving All with No Parameters: Adaptive NormalHedge | cs.LG | We study the classic online learning problem of predicting with expert
advice, and propose a truly parameter-free and adaptive algorithm that achieves
several objectives simultaneously without using any prior information. The main
component of this work is an improved version of the NormalHedge.DT algorithm
(Luo and Sc... | computer science |
32,355 | SDCA without Duality | cs.LG | Stochastic Dual Coordinate Ascent is a popular method for solving regularized
loss minimization for the case of convex losses. In this paper we show how a
variant of SDCA can be applied for non-convex losses. We prove linear
convergence rate even if individual loss functions are non-convex as long as
the expected loss ... | computer science |
32,356 | Teaching and compressing for low VC-dimension | cs.LG | In this work we study the quantitative relation between VC-dimension and two
other basic parameters related to learning and teaching. Namely, the quality of
sample compression schemes and of teaching sets for classes of low
VC-dimension. Let $C$ be a binary concept class of size $m$ and VC-dimension
$d$. Prior to this ... | computer science |
32,357 | Contextual Dueling Bandits | cs.LG | We consider the problem of learning to choose actions using contextual
information when provided with limited feedback in the form of relative
pairwise comparisons. We study this problem in the dueling-bandits framework of
Yue et al. (2009), which we extend to incorporate context. Roughly, the
learner's goal is to find... | computer science |
32,358 | Reified Context Models | cs.LG | A classic tension exists between exact inference in a simple model and
approximate inference in a complex model. The latter offers expressivity and
thus accuracy, but the former provides coverage of the space, an important
property for confidence estimation and learning with indirect supervision. In
this work, we intro... | computer science |
32,359 | Learning Fast-Mixing Models for Structured Prediction | cs.LG | Markov Chain Monte Carlo (MCMC) algorithms are often used for approximate
inference inside learning, but their slow mixing can be difficult to diagnose
and the approximations can seriously degrade learning. To alleviate these
issues, we define a new model family using strong Doeblin Markov chains, whose
mixing times ca... | computer science |
32,360 | Strongly Adaptive Online Learning | cs.LG | Strongly adaptive algorithms are algorithms whose performance on every time
interval is close to optimal. We present a reduction that can transform
standard low-regret algorithms to strongly adaptive. As a consequence, we
derive simple, yet efficient, strongly adaptive algorithms for a handful of
problems. | computer science |
32,361 | The VC-Dimension of Similarity Hypotheses Spaces | cs.LG | Given a set $X$ and a function $h:X\longrightarrow\{0,1\}$ which labels each
element of $X$ with either $0$ or $1$, we may define a function $h^{(s)}$ to
measure the similarity of pairs of points in $X$ according to $h$.
Specifically, for $h\in \{0,1\}^X$ we define $h^{(s)}\in \{0,1\}^{X\times X}$
by $h^{(s)}(w,x):= \m... | computer science |
32,362 | Online Learning with Feedback Graphs: Beyond Bandits | cs.LG | We study a general class of online learning problems where the feedback is
specified by a graph. This class includes online prediction with expert advice
and the multi-armed bandit problem, but also several learning problems where
the online player does not necessarily observe his own loss. We analyze how the
structure... | computer science |
32,363 | Learning Mixtures of Gaussians in High Dimensions | cs.LG | Efficiently learning mixture of Gaussians is a fundamental problem in
statistics and learning theory. Given samples coming from a random one out of k
Gaussian distributions in Rn, the learning problem asks to estimate the means
and the covariance matrices of these Gaussians. This learning problem arises in
many areas r... | computer science |
32,364 | Utility-Theoretic Ranking for Semi-Automated Text Classification | cs.LG | \emph{Semi-Automated Text Classification} (SATC) may be defined as the task
of ranking a set $\mathcal{D}$ of automatically labelled textual documents in
such a way that, if a human annotator validates (i.e., inspects and corrects
where appropriate) the documents in a top-ranked portion of $\mathcal{D}$ with
the goal o... | computer science |
32,365 | An $\mathcal{O}(n\log n)$ projection operator for weighted $\ell_1$-norm
regularization with sum constraint | cs.LG | We provide a simple and efficient algorithm for the projection operator for
weighted $\ell_1$-norm regularization subject to a sum constraint, together
with an elementary proof. The implementation of the proposed algorithm can be
downloaded from the author's homepage. | computer science |
32,366 | Projection onto the capped simplex | cs.LG | We provide a simple and efficient algorithm for computing the Euclidean
projection of a point onto the capped simplex---a simplex with an additional
uniform bound on each coordinate---together with an elementary proof. Both the
MATLAB and C++ implementations of the proposed algorithm can be downloaded at
https://eng.uc... | computer science |
32,367 | Joint Active Learning and Feature Selection via CUR Matrix Decomposition | cs.LG | This paper focuses on the problem of simultaneous sample and feature
selection for machine learning in a fully unsupervised setting. Though most
existing works tackle these two problems separately that derives two
well-studied sub-areas namely active learning and feature selection, a unified
approach is inspirational s... | computer science |
32,368 | Probabilistic Label Relation Graphs with Ising Models | cs.LG | We consider classification problems in which the label space has structure. A
common example is hierarchical label spaces, corresponding to the case where
one label subsumes another (e.g., animal subsumes dog). But labels can also be
mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To
jointl... | computer science |
32,369 | Ranking and significance of variable-length similarity-based time series
motifs | cs.LG | The detection of very similar patterns in a time series, commonly called
motifs, has received continuous and increasing attention from diverse
scientific communities. In particular, recent approaches for discovering
similar motifs of different lengths have been proposed. In this work, we show
that such variable-length ... | computer science |
32,370 | Model selection of polynomial kernel regression | cs.LG | Polynomial kernel regression is one of the standard and state-of-the-art
learning strategies. However, as is well known, the choices of the degree of
polynomial kernel and the regularization parameter are still open in the realm
of model selection. The first aim of this paper is to develop a strategy to
select these pa... | computer science |
32,371 | Label optimal regret bounds for online local learning | cs.LG | We resolve an open question from (Christiano, 2014b) posed in COLT'14
regarding the optimal dependency of the regret achievable for online local
learning on the size of the label set. In this framework the algorithm is shown
a pair of items at each step, chosen from a set of $n$ items. The learner then
predicts a label... | computer science |
32,372 | Deep Learning and the Information Bottleneck Principle | cs.LG | Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the
information bottleneck (IB) principle. We first show that any DNN can be
quantified by the mutual information between the layers and the input and
output variables. Using this representation we can calculate the optimal
information theoretic ... | computer science |
32,373 | apsis - Framework for Automated Optimization of Machine Learning Hyper
Parameters | cs.LG | The apsis toolkit presented in this paper provides a flexible framework for
hyperparameter optimization and includes both random search and a bayesian
optimizer. It is implemented in Python and its architecture features
adaptability to any desired machine learning code. It can easily be used with
common Python ML frame... | computer science |
32,374 | Scalable Discovery of Time-Series Shapelets | cs.LG | Time-series classification is an important problem for the data mining
community due to the wide range of application domains involving time-series
data. A recent paradigm, called shapelets, represents patterns that are highly
predictive for the target variable. Shapelets are discovered by measuring the
prediction accu... | computer science |
32,375 | Estimating the Mean Number of K-Means Clusters to Form | cs.LG | Utilizing the sample size of a dataset, the random cluster model is employed
in order to derive an estimate of the mean number of K-Means clusters to form
during classification of a dataset. | computer science |
32,376 | LINE: Large-scale Information Network Embedding | cs.LG | This paper studies the problem of embedding very large information networks
into low-dimensional vector spaces, which is useful in many tasks such as
visualization, node classification, and link prediction. Most existing graph
embedding methods do not scale for real world information networks which
usually contain mill... | computer science |
32,377 | An Emphatic Approach to the Problem of Off-policy Temporal-Difference
Learning | cs.LG | In this paper we introduce the idea of improving the performance of
parametric temporal-difference (TD) learning algorithms by selectively
emphasizing or de-emphasizing their updates on different time steps. In
particular, we show that varying the emphasis of linear TD($\lambda$)'s updates
in a particular way causes it... | computer science |
32,378 | On Extreme Pruning of Random Forest Ensembles for Real-time Predictive
Applications | cs.LG | Random Forest (RF) is an ensemble supervised machine learning technique that
was developed by Breiman over a decade ago. Compared with other ensemble
techniques, it has proved its accuracy and superiority. Many researchers,
however, believe that there is still room for enhancing and improving its
performance accuracy. ... | computer science |
32,379 | Ultra-Fast Shapelets for Time Series Classification | cs.LG | Time series shapelets are discriminative subsequences and their similarity to
a time series can be used for time series classification. Since the discovery
of time series shapelets is costly in terms of time, the applicability on long
or multivariate time series is difficult. In this work we propose Ultra-Fast
Shapelet... | computer science |
32,380 | An Outlier Detection-based Tree Selection Approach to Extreme Pruning of
Random Forests | cs.LG | Random Forest (RF) is an ensemble classification technique that was developed
by Breiman over a decade ago. Compared with other ensemble techniques, it has
proved its accuracy and superiority. Many researchers, however, believe that
there is still room for enhancing and improving its performance in terms of
predictive ... | computer science |
32,381 | GSNs : Generative Stochastic Networks | cs.LG | We introduce a novel training principle for probabilistic models that is an
alternative to maximum likelihood. The proposed Generative Stochastic Networks
(GSN) framework is based on learning the transition operator of a Markov chain
whose stationary distribution estimates the data distribution. Because the
transition ... | computer science |
32,382 | On Invariance and Selectivity in Representation Learning | cs.LG | We discuss data representation which can be learned automatically from data,
are invariant to transformations, and at the same time selective, in the sense
that two points have the same representation only if they are one the
transformation of the other. The mathematical results here sharpen some of the
key claims of i... | computer science |
32,383 | Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies | cs.LG | In this paper, we investigate a largely extended version of classical MAB
problem, called networked combinatorial bandit problems. In particular, we
consider the setting of a decision maker over a networked bandits as follows:
each time a combinatorial strategy, e.g., a group of arms, is chosen, and the
decision maker ... | computer science |
32,384 | Optimum Reject Options for Prototype-based Classification | cs.LG | We analyse optimum reject strategies for prototype-based classifiers and
real-valued rejection measures, using the distance of a data point to the
closest prototype or probabilistic counterparts. We compare reject schemes with
global thresholds, and local thresholds for the Voronoi cells of the
classifier. For the latt... | computer science |
32,385 | Proficiency Comparison of LADTree and REPTree Classifiers for Credit
Risk Forecast | cs.LG | Predicting the Credit Defaulter is a perilous task of Financial Industries
like Banks. Ascertaining non-payer before giving loan is a significant and
conflict-ridden task of the Banker. Classification techniques are the better
choice for predictive analysis like finding the claimant, whether he/she is an
unpretentious ... | computer science |
32,386 | Fusing Continuous-valued Medical Labels using a Bayesian Model | cs.LG | With the rapid increase in volume of time series medical data available
through wearable devices, there is a need to employ automated algorithms to
label data. Examples of labels include interventions, changes in activity (e.g.
sleep) and changes in physiology (e.g. arrhythmias). However, automated
algorithms tend to b... | computer science |
32,387 | A Probabilistic Interpretation of Sampling Theory of Graph Signals | cs.LG | We give a probabilistic interpretation of sampling theory of graph signals.
To do this, we first define a generative model for the data using a pairwise
Gaussian random field (GRF) which depends on the graph. We show that, under
certain conditions, reconstructing a graph signal from a subset of its samples
by least squ... | computer science |
32,388 | Online classifier adaptation for cost-sensitive learning | cs.LG | In this paper, we propose the problem of online cost-sensitive clas- sifier
adaptation and the first algorithm to solve it. We assume we have a base
classifier for a cost-sensitive classification problem, but it is trained with
respect to a cost setting different to the desired one. Moreover, we also have
some training... | computer science |
32,389 | Communication Efficient Distributed Kernel Principal Component Analysis | cs.LG | Kernel Principal Component Analysis (KPCA) is a key machine learning
algorithm for extracting nonlinear features from data. In the presence of a
large volume of high dimensional data collected in a distributed fashion, it
becomes very costly to communicate all of this data to a single data center and
then perform kerne... | computer science |
32,390 | Comparing published multi-label classifier performance measures to the
ones obtained by a simple multi-label baseline classifier | cs.LG | In supervised learning, simple baseline classifiers can be constructed by
only looking at the class, i.e., ignoring any other information from the
dataset. The single-label learning community frequently uses as a reference the
one which always predicts the majority class. Although a classifier might
perform worse than ... | computer science |
32,391 | Sample compression schemes for VC classes | cs.LG | Sample compression schemes were defined by Littlestone and Warmuth (1986) as
an abstraction of the structure underlying many learning algorithms. Roughly
speaking, a sample compression scheme of size $k$ means that given an arbitrary
list of labeled examples, one can retain only $k$ of them in a way that allows
to reco... | computer science |
32,392 | A Survey of Classification Techniques in the Area of Big Data | cs.LG | Big Data concern large-volume, growing data sets that are complex and have
multiple autonomous sources. Earlier technologies were not able to handle
storage and processing of huge data thus Big Data concept comes into existence.
This is a tedious job for users unstructured data. So, there should be some
mechanism which... | computer science |
32,393 | Multi-Labeled Classification of Demographic Attributes of Patients: a
case study of diabetics patients | cs.LG | Automated learning of patients demographics can be seen as multi-label
problem where a patient model is based on different race and gender groups. The
resulting model can be further integrated into Privacy-Preserving Data Mining,
where it can be used to assess risk of identification of different patient
groups. Our pro... | computer science |
32,394 | A Variance Reduced Stochastic Newton Method | cs.LG | Quasi-Newton methods are widely used in practise for convex loss minimization
problems. These methods exhibit good empirical performance on a wide variety of
tasks and enjoy super-linear convergence to the optimal solution. For
large-scale learning problems, stochastic Quasi-Newton methods have been
recently proposed. ... | computer science |
32,395 | Global Bandits | cs.LG | Multi-armed bandits (MAB) model sequential decision making problems, in which
a learner sequentially chooses arms with unknown reward distributions in order
to maximize its cumulative reward. Most of the prior work on MAB assumes that
the reward distributions of each arm are independent. But in a wide variety of
decisi... | computer science |
32,396 | Towards More Efficient SPSD Matrix Approximation and CUR Matrix
Decomposition | cs.LG | Symmetric positive semi-definite (SPSD) matrix approximation methods have
been extensively used to speed up large-scale eigenvalue computation and kernel
learning methods. The standard sketch based method, which we call the prototype
model, produces relatively accurate approximations, but is inefficient on large
square... | computer science |
32,397 | Fast Label Embeddings for Extremely Large Output Spaces | cs.LG | Many modern multiclass and multilabel problems are characterized by
increasingly large output spaces. For these problems, label embeddings have
been shown to be a useful primitive that can improve computational and
statistical efficiency. In this work we utilize a correspondence between rank
constrained estimation and ... | computer science |
32,398 | Generalized Categorization Axioms | cs.LG | Categorization axioms have been proposed to axiomatizing clustering results,
which offers a hint of bridging the difference between human recognition system
and machine learning through an intuitive observation: an object should be
assigned to its most similar category. However, categorization axioms cannot be
generali... | computer science |
32,399 | Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using
Householder Reflections | cs.LG | The problem of learning long-term dependencies in sequences using Recurrent
Neural Networks (RNNs) is still a major challenge. Recent methods have been
suggested to solve this problem by constraining the transition matrix to be
unitary during training which ensures that its norm is equal to one and
prevents exploding g... | computer science |
32,400 | Higher Order Mutual Information Approximation for Feature Selection | cs.LG | Feature selection is a process of choosing a subset of relevant features so
that the quality of prediction models can be improved. An extensive body of
work exists on information-theoretic feature selection, based on maximizing
Mutual Information (MI) between subsets of features and class labels. The prior
methods use ... | computer science |
32,401 | Predictive Clinical Decision Support System with RNN Encoding and Tensor
Decoding | cs.LG | With the introduction of the Electric Health Records, large amounts of
digital data become available for analysis and decision support. When
physicians are prescribing treatments to a patient, they need to consider a
large range of data variety and volume, making decisions increasingly complex.
Machine learning based C... | computer science |
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