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31,802 | Online Learning of Noisy Data with Kernels | cs.LG | We study online learning when individual instances are corrupted by
adversarially chosen random noise. We assume the noise distribution is unknown,
and may change over time with no restriction other than having zero mean and
bounded variance. Our technique relies on a family of unbiased estimators for
non-linear functi... | computer science |
31,803 | Evolution with Drifting Targets | cs.LG | We consider the question of the stability of evolutionary algorithms to
gradual changes, or drift, in the target concept. We define an algorithm to be
resistant to drift if, for some inverse polynomial drift rate in the target
function, it converges to accuracy 1 -- \epsilon , with polynomial resources,
and then stays ... | computer science |
31,804 | Learning Kernel-Based Halfspaces with the Zero-One Loss | cs.LG | We describe and analyze a new algorithm for agnostically learning
kernel-based halfspaces with respect to the \emph{zero-one} loss function.
Unlike most previous formulations which rely on surrogate convex loss functions
(e.g. hinge-loss in SVM and log-loss in logistic regression), we provide finite
time/sample guarant... | computer science |
31,805 | On the clustering aspect of nonnegative matrix factorization | cs.LG | This paper provides a theoretical explanation on the clustering aspect of
nonnegative matrix factorization (NMF). We prove that even without imposing
orthogonality nor sparsity constraint on the basis and/or coefficient matrix,
NMF still can give clustering results, thus providing a theoretical support for
many works, ... | computer science |
31,806 | Multi-View Active Learning in the Non-Realizable Case | cs.LG | The sample complexity of active learning under the realizability assumption
has been well-studied. The realizability assumption, however, rarely holds in
practice. In this paper, we theoretically characterize the sample complexity of
active learning in the non-realizable case under multi-view setting. We prove
that, wi... | computer science |
31,807 | Prediction with Advice of Unknown Number of Experts | cs.LG | In the framework of prediction with expert advice, we consider a recently
introduced kind of regret bounds: the bounds that depend on the effective
instead of nominal number of experts. In contrast to the NormalHedge bound,
which mainly depends on the effective number of experts and also weakly depends
on the nominal o... | computer science |
31,808 | Predictive PAC learnability: a paradigm for learning from exchangeable
input data | cs.LG | Exchangeable random variables form an important and well-studied
generalization of i.i.d. variables, however simple examples show that no
nontrivial concept or function classes are PAC learnable under general
exchangeable data inputs $X_1,X_2,\ldots$. Inspired by the work of Berti and
Rigo on a Glivenko--Cantelli theor... | computer science |
31,809 | Regression on fixed-rank positive semidefinite matrices: a Riemannian
approach | cs.LG | The paper addresses the problem of learning a regression model parameterized
by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear
nature of the search space and on scalability to high-dimensional problems. The
mathematical developments rely on the theory of gradient descent algorithms
adapted to ... | computer science |
31,810 | Dyadic Prediction Using a Latent Feature Log-Linear Model | cs.LG | In dyadic prediction, labels must be predicted for pairs (dyads) whose
members possess unique identifiers and, sometimes, additional features called
side-information. Special cases of this problem include collaborative filtering
and link prediction. We present the first model for dyadic prediction that
satisfies severa... | computer science |
31,811 | Agnostic Active Learning Without Constraints | cs.LG | We present and analyze an agnostic active learning algorithm that works
without keeping a version space. This is unlike all previous approaches where a
restricted set of candidate hypotheses is maintained throughout learning, and
only hypotheses from this set are ever returned. By avoiding this version space
approach, ... | computer science |
31,812 | Extension of Wirtinger's Calculus to Reproducing Kernel Hilbert Spaces
and the Complex Kernel LMS | cs.LG | Over the last decade, kernel methods for nonlinear processing have
successfully been used in the machine learning community. The primary
mathematical tool employed in these methods is the notion of the Reproducing
Kernel Hilbert Space. However, so far, the emphasis has been on batch
techniques. It is only recently, tha... | computer science |
31,813 | MINLIP for the Identification of Monotone Wiener Systems | cs.LG | This paper studies the MINLIP estimator for the identification of Wiener
systems consisting of a sequence of a linear FIR dynamical model, and a
monotonically increasing (or decreasing) static function. Given $T$
observations, this algorithm boils down to solving a convex quadratic program
with $O(T)$ variables and ine... | computer science |
31,814 | PAC learnability of a concept class under non-atomic measures: a problem
by Vidyasagar | cs.LG | In response to a 1997 problem of M. Vidyasagar, we state a necessary and
sufficient condition for distribution-free PAC learnability of a concept class
$\mathscr C$ under the family of all non-atomic (diffuse) measures on the
domain $\Omega$. Clearly, finiteness of the classical Vapnik-Chervonenkis
dimension of $\maths... | computer science |
31,815 | Exploring Language-Independent Emotional Acoustic Features via Feature
Selection | cs.LG | We propose a novel feature selection strategy to discover
language-independent acoustic features that tend to be responsible for emotions
regardless of languages, linguistics and other factors. Experimental results
suggest that the language-independent feature subset discovered yields the
performance comparable to the ... | computer science |
31,816 | Fast Overlapping Group Lasso | cs.LG | The group Lasso is an extension of the Lasso for feature selection on
(predefined) non-overlapping groups of features. The non-overlapping group
structure limits its applicability in practice. There have been several recent
attempts to study a more general formulation, where groups of features are
given, potentially wi... | computer science |
31,817 | Reinforcement Learning by Comparing Immediate Reward | cs.LG | This paper introduces an approach to Reinforcement Learning Algorithm by
comparing their immediate rewards using a variation of Q-Learning algorithm.
Unlike the conventional Q-Learning, the proposed algorithm compares current
reward with immediate reward of past move and work accordingly. Relative reward
based Q-learni... | computer science |
31,818 | A Unified View of Regularized Dual Averaging and Mirror Descent with
Implicit Updates | cs.LG | We study three families of online convex optimization algorithms:
follow-the-proximally-regularized-leader (FTRL-Proximal), regularized dual
averaging (RDA), and composite-objective mirror descent. We first prove
equivalence theorems that show all of these algorithms are instantiations of a
general FTRL update. This pr... | computer science |
31,819 | Conditional Random Fields and Support Vector Machines: A Hybrid Approach | cs.LG | We propose a novel hybrid loss for multiclass and structured prediction
problems that is a convex combination of log loss for Conditional Random Fields
(CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We
provide a sufficient condition for when the hybrid loss is Fisher consistent
for classificatio... | computer science |
31,820 | Geometric Decision Tree | cs.LG | In this paper we present a new algorithm for learning oblique decision trees.
Most of the current decision tree algorithms rely on impurity measures to
assess the goodness of hyperplanes at each node while learning a decision tree
in a top-down fashion. These impurity measures do not properly capture the
geometric stru... | computer science |
31,821 | On the Doubt about Margin Explanation of Boosting | cs.LG | Margin theory provides one of the most popular explanations to the success of
\texttt{AdaBoost}, where the central point lies in the recognition that
\textit{margin} is the key for characterizing the performance of
\texttt{AdaBoost}. This theory has been very influential, e.g., it has been
used to argue that \texttt{Ad... | computer science |
31,822 | Totally Corrective Multiclass Boosting with Binary Weak Learners | cs.LG | In this work, we propose a new optimization framework for multiclass boosting
learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two
successful multiclass boosting algorithms, which can use binary weak learners.
We explicitly derive these two algorithms' Lagrange dual problems based on
their regularized ... | computer science |
31,823 | Optimistic Rates for Learning with a Smooth Loss | cs.LG | We establish an excess risk bound of O(H R_n^2 + R_n \sqrt{H L*}) for
empirical risk minimization with an H-smooth loss function and a hypothesis
class with Rademacher complexity R_n, where L* is the best risk achievable by
the hypothesis class. For typical hypothesis classes where R_n = \sqrt{R/n},
this translates to ... | computer science |
31,824 | Efficient L1/Lq Norm Regularization | cs.LG | Sparse learning has recently received increasing attention in many areas
including machine learning, statistics, and applied mathematics. The mixed-norm
regularization based on the L1/Lq norm with q > 1 is attractive in many
applications of regression and classification in that it facilitates group
sparsity in the mode... | computer science |
31,825 | Multi-parametric Solution-path Algorithm for Instance-weighted Support
Vector Machines | cs.LG | An instance-weighted variant of the support vector machine (SVM) has
attracted considerable attention recently since they are useful in various
machine learning tasks such as non-stationary data analysis, heteroscedastic
data modeling, transfer learning, learning to rank, and transduction. An
important challenge in the... | computer science |
31,826 | Portfolio Allocation for Bayesian Optimization | cs.LG | Bayesian optimization with Gaussian processes has become an increasingly
popular tool in the machine learning community. It is efficient and can be used
when very little is known about the objective function, making it popular in
expensive black-box optimization scenarios. It uses Bayesian methods to sample
the objecti... | computer science |
31,827 | Fast Reinforcement Learning for Energy-Efficient Wireless Communications | cs.LG | We consider the problem of energy-efficient point-to-point transmission of
delay-sensitive data (e.g. multimedia data) over a fading channel. Existing
research on this topic utilizes either physical-layer centric solutions, namely
power-control and adaptive modulation and coding (AMC), or system-level
solutions based o... | computer science |
31,828 | The Attentive Perceptron | cs.LG | We propose a focus of attention mechanism to speed up the Perceptron
algorithm. Focus of attention speeds up the Perceptron algorithm by lowering
the number of features evaluated throughout training and prediction. Whereas
the traditional Perceptron evaluates all the features of each example, the
Attentive Perceptron e... | computer science |
31,829 | Regularized Risk Minimization by Nesterov's Accelerated Gradient
Methods: Algorithmic Extensions and Empirical Studies | cs.LG | Nesterov's accelerated gradient methods (AGM) have been successfully applied
in many machine learning areas. However, their empirical performance on
training max-margin models has been inferior to existing specialized solvers.
In this paper, we first extend AGM to strongly convex and composite objective
functions with ... | computer science |
31,830 | Online Importance Weight Aware Updates | cs.LG | An importance weight quantifies the relative importance of one example over
another, coming up in applications of boosting, asymmetric classification
costs, reductions, and active learning. The standard approach for dealing with
importance weights in gradient descent is via multiplication of the gradient.
We first demo... | computer science |
31,831 | On Theorem 2.3 in "Prediction, Learning, and Games" by Cesa-Bianchi and
Lugosi | cs.LG | The note presents a modified proof of a loss bound for the exponentially
weighted average forecaster with time-varying potential. The regret term of the
algorithm is upper-bounded by sqrt{n ln(N)} (uniformly in n), where N is the
number of experts and n is the number of steps. | computer science |
31,832 | Estimating Probabilities in Recommendation Systems | cs.LG | Recommendation systems are emerging as an important business application with
significant economic impact. Currently popular systems include Amazon's book
recommendations, Netflix's movie recommendations, and Pandora's music
recommendations. In this paper we address the problem of estimating
probabilities associated wi... | computer science |
31,833 | A Tutorial on Bayesian Optimization of Expensive Cost Functions, with
Application to Active User Modeling and Hierarchical Reinforcement Learning | cs.LG | We present a tutorial on Bayesian optimization, a method of finding the
maximum of expensive cost functions. Bayesian optimization employs the Bayesian
technique of setting a prior over the objective function and combining it with
evidence to get a posterior function. This permits a utility-based selection of
the next ... | computer science |
31,834 | Queue-Aware Dynamic Clustering and Power Allocation for Network MIMO
Systems via Distributive Stochastic Learning | cs.LG | In this paper, we propose a two-timescale delay-optimal dynamic clustering
and power allocation design for downlink network MIMO systems. The dynamic
clustering control is adaptive to the global queue state information (GQSI)
only and computed at the base station controller (BSC) over a longer time
scale. On the other ... | computer science |
31,835 | Survey & Experiment: Towards the Learning Accuracy | cs.LG | To attain the best learning accuracy, people move on with difficulties and
frustrations. Though one can optimize the empirical objective using a given set
of samples, its generalization ability to the entire sample distribution
remains questionable. Even if a fair generalization guarantee is offered, one
still wants to... | computer science |
31,836 | Travel Time Estimation Using Floating Car Data | cs.LG | This report explores the use of machine learning techniques to accurately
predict travel times in city streets and highways using floating car data
(location information of user vehicles on a road network). The aim of this
report is twofold, first we present a general architecture of solving this
problem, then present ... | computer science |
31,837 | How I won the "Chess Ratings - Elo vs the Rest of the World" Competition | cs.LG | This article discusses in detail the rating system that won the kaggle
competition "Chess Ratings: Elo vs the rest of the world". The competition
provided a historical dataset of outcomes for chess games, and aimed to
discover whether novel approaches can predict the outcomes of future games,
more accurately than the w... | computer science |
31,838 | EigenNet: A Bayesian hybrid of generative and conditional models for
sparse learning | cs.LG | It is a challenging task to select correlated variables in a high dimensional
space. To address this challenge, the elastic net has been developed and
successfully applied to many applications. Despite its great success, the
elastic net does not explicitly use correlation information embedded in data to
select correlat... | computer science |
31,839 | Transductive Ordinal Regression | cs.LG | Ordinal regression is commonly formulated as a multi-class problem with
ordinal constraints. The challenge of designing accurate classifiers for
ordinal regression generally increases with the number of classes involved, due
to the large number of labeled patterns that are needed. The availability of
ordinal class labe... | computer science |
31,840 | Learning transformed product distributions | cs.LG | We consider the problem of learning an unknown product distribution $X$ over
$\{0,1\}^n$ using samples $f(X)$ where $f$ is a \emph{known} transformation
function. Each choice of a transformation function $f$ specifies a learning
problem in this framework.
Information-theoretic arguments show that for every transforma... | computer science |
31,841 | A Feature Selection Method for Multivariate Performance Measures | cs.LG | Feature selection with specific multivariate performance measures is the key
to the success of many applications, such as image retrieval and text
classification. The existing feature selection methods are usually designed for
classification error. In this paper, we propose a generalized sparse
regularizer. Based on th... | computer science |
31,842 | Parallel Online Learning | cs.LG | In this work we study parallelization of online learning, a core primitive in
machine learning. In a parallel environment all known approaches for parallel
online learning lead to delayed updates, where the model is updated using
out-of-date information. In the worst case, or when examples are temporally
correlated, de... | computer science |
31,843 | Suboptimal Solution Path Algorithm for Support Vector Machine | cs.LG | We consider a suboptimal solution path algorithm for the Support Vector
Machine. The solution path algorithm is an effective tool for solving a
sequence of a parametrized optimization problems in machine learning. The path
of the solutions provided by this algorithm are very accurate and they satisfy
the optimality con... | computer science |
31,844 | Domain Adaptation: Overfitting and Small Sample Statistics | cs.LG | We study the prevalent problem when a test distribution differs from the
training distribution. We consider a setting where our training set consists of
a small number of sample domains, but where we have many samples in each
domain. Our goal is to generalize to a new domain. For example, we may want to
learn a similar... | computer science |
31,845 | Adaptively Learning the Crowd Kernel | cs.LG | We introduce an algorithm that, given n objects, learns a similarity matrix
over all n^2 pairs, from crowdsourced data alone. The algorithm samples
responses to adaptively chosen triplet-based relative-similarity queries. Each
query has the form "is object 'a' more similar to 'b' or to 'c'?" and is chosen
to be maximal... | computer science |
31,846 | A Maximal Large Deviation Inequality for Sub-Gaussian Variables | cs.LG | In this short note we prove a maximal concentration lemma for sub-Gaussian
random variables stating that for independent sub-Gaussian random variables we
have \[P<(\max_{1\le i\le N}S_{i}>\epsilon>)
\le\exp<(-\frac{1}{N^2}\sum_{i=1}^{N}\frac{\epsilon^{2}}{2\sigma_{i}^{2}}>), \]
where $S_i$ is the sum of $i$ zero mean i... | computer science |
31,847 | Calibration with Changing Checking Rules and Its Application to
Short-Term Trading | cs.LG | We provide a natural learning process in which a financial trader without a
risk receives a gain in case when Stock Market is inefficient. In this process,
the trader rationally choose his gambles using a prediction made by a
randomized calibrated algorithm. Our strategy is based on Dawid's notion of
calibration with m... | computer science |
31,848 | Bounding the Fat Shattering Dimension of a Composition Function Class
Built Using a Continuous Logic Connective | cs.LG | We begin this report by describing the Probably Approximately Correct (PAC)
model for learning a concept class, consisting of subsets of a domain, and a
function class, consisting of functions from the domain to the unit interval.
Two combinatorial parameters, the Vapnik-Chervonenkis (VC) dimension and its
generalizati... | computer science |
31,849 | Online Learning, Stability, and Stochastic Gradient Descent | cs.LG | In batch learning, stability together with existence and uniqueness of the
solution corresponds to well-posedness of Empirical Risk Minimization (ERM)
methods; recently, it was proved that CV_loo stability is necessary and
sufficient for generalization and consistency of ERM. In this note, we
introduce CV_on stability,... | computer science |
31,850 | Large-Scale Music Annotation and Retrieval: Learning to Rank in Joint
Semantic Spaces | cs.LG | Music prediction tasks range from predicting tags given a song or clip of
audio, predicting the name of the artist, or predicting related songs given a
song, clip, artist name or tag. That is, we are interested in every semantic
relationship between the different musical concepts in our database. In
realistically sized... | computer science |
31,851 | Kernel Belief Propagation | cs.LG | We propose a nonparametric generalization of belief propagation, Kernel
Belief Propagation (KBP), for pairwise Markov random fields. Messages are
represented as functions in a reproducing kernel Hilbert space (RKHS), and
message updates are simple linear operations in the RKHS. KBP makes none of the
assumptions commonl... | computer science |
31,852 | The Perceptron with Dynamic Margin | cs.LG | The classical perceptron rule provides a varying upper bound on the maximum
margin, namely the length of the current weight vector divided by the total
number of updates up to that time. Requiring that the perceptron updates its
internal state whenever the normalized margin of a pattern is found not to
exceed a certain... | computer science |
31,853 | A Unified Framework for Approximating and Clustering Data | cs.LG | Given a set $F$ of $n$ positive functions over a ground set $X$, we consider
the problem of computing $x^*$ that minimizes the expression $\sum_{f\in
F}f(x)$, over $x\in X$. A typical application is \emph{shape fitting}, where we
wish to approximate a set $P$ of $n$ elements (say, points) by a shape $x$ from
a (possibl... | computer science |
31,854 | Max-Margin Stacking and Sparse Regularization for Linear Classifier
Combination and Selection | cs.LG | The main principle of stacked generalization (or Stacking) is using a
second-level generalizer to combine the outputs of base classifiers in an
ensemble. In this paper, we investigate different combination types under the
stacking framework; namely weighted sum (WS), class-dependent weighted sum
(CWS) and linear stacke... | computer science |
31,855 | Reinforcement learning based sensing policy optimization for energy
efficient cognitive radio networks | cs.LG | This paper introduces a machine learning based collaborative multi-band
spectrum sensing policy for cognitive radios. The proposed sensing policy
guides secondary users to focus the search of unused radio spectrum to those
frequencies that persistently provide them high data rate. The proposed policy
is based on machin... | computer science |
31,856 | Learning the Dependence Graph of Time Series with Latent Factors | cs.LG | This paper considers the problem of learning, from samples, the dependency
structure of a system of linear stochastic differential equations, when some of
the variables are latent. In particular, we observe the time evolution of some
variables, and never observe other variables; from this, we would like to find
the dep... | computer science |
31,857 | On epsilon-optimality of the pursuit learning algorithm | cs.LG | Estimator algorithms in learning automata are useful tools for adaptive,
real-time optimization in computer science and engineering applications. This
paper investigates theoretical convergence properties for a special case of
estimator algorithms: the pursuit learning algorithm. In this note, we identify
and fill a ga... | computer science |
31,858 | Decoding finger movements from ECoG signals using switching linear
models | cs.LG | One of the major challenges of ECoG-based Brain-Machine Interfaces is the
movement prediction of a human subject. Several methods exist to predict an arm
2-D trajectory. The fourth BCI Competition gives a dataset in which the aim is
to predict individual finger movements (5-D trajectory). The difficulty lies in
the fac... | computer science |
31,859 | Large margin filtering for signal sequence labeling | cs.LG | Signal Sequence Labeling consists in predicting a sequence of labels given an
observed sequence of samples. A naive way is to filter the signal in order to
reduce the noise and to apply a classification algorithm on the filtered
samples. We propose in this paper to jointly learn the filter with the
classifier leading t... | computer science |
31,860 | Handling uncertainties in SVM classification | cs.LG | This paper addresses the pattern classification problem arising when
available target data include some uncertainty information. Target data
considered here is either qualitative (a class label) or quantitative (an
estimation of the posterior probability). Our main contribution is a SVM
inspired formulation of this pro... | computer science |
31,861 | Algorithmic Programming Language Identification | cs.LG | Motivated by the amount of code that goes unidentified on the web, we
introduce a practical method for algorithmically identifying the programming
language of source code. Our work is based on supervised learning and
intelligent statistical features. We also explored, but abandoned, a
grammatical approach. In testing, ... | computer science |
31,862 | Better Mini-Batch Algorithms via Accelerated Gradient Methods | cs.LG | Mini-batch algorithms have been proposed as a way to speed-up stochastic
convex optimization problems. We study how such algorithms can be improved
using accelerated gradient methods. We provide a novel analysis, which shows
how standard gradient methods may sometimes be insufficient to obtain a
significant speed-up an... | computer science |
31,863 | Potential-Based Shaping and Q-Value Initialization are Equivalent | cs.LG | Shaping has proven to be a powerful but precarious means of improving
reinforcement learning performance. Ng, Harada, and Russell (1999) proposed the
potential-based shaping algorithm for adding shaping rewards in a way that
guarantees the learner will learn optimal behavior. In this note, we prove
certain similarities... | computer science |
31,864 | IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine
Learning | cs.LG | We present IBSEAD or distributed autonomous entity systems based Interaction
- a learning algorithm for the computer to self-evolve in a self-obsessed
manner. This learning algorithm will present the computer to look at the
internal and external environment in series of independent entities, which will
interact with ea... | computer science |
31,865 | A Note on Improved Loss Bounds for Multiple Kernel Learning | cs.LG | In this paper, we correct an upper bound, presented in~\cite{hs-11}, on the
generalisation error of classifiers learned through multiple kernel learning.
The bound in~\cite{hs-11} uses Rademacher complexity and has an\emph{additive}
dependence on the logarithm of the number of kernels and the margin achieved by
the cla... | computer science |
31,866 | Feature Extraction for Change-Point Detection using Stationary Subspace
Analysis | cs.LG | Detecting changes in high-dimensional time series is difficult because it
involves the comparison of probability densities that need to be estimated from
finite samples. In this paper, we present the first feature extraction method
tailored to change point detection, which is based on an extended version of
Stationary ... | computer science |
31,867 | Optimal Algorithms for Ridge and Lasso Regression with Partially
Observed Attributes | cs.LG | We consider the most common variants of linear regression, including Ridge,
Lasso and Support-vector regression, in a setting where the learner is allowed
to observe only a fixed number of attributes of each example at training time.
We present simple and efficient algorithms for these problems: for Lasso and
Ridge reg... | computer science |
31,868 | Non-trivial two-armed partial-monitoring games are bandits | cs.LG | We consider online learning in partial-monitoring games against an oblivious
adversary. We show that when the number of actions available to the learner is
two and the game is nontrivial then it is reducible to a bandit-like game and
thus the minimax regret is $\Theta(\sqrt{T})$. | computer science |
31,869 | Active Learning with Multiple Views | cs.LG | Active learners alleviate the burden of labeling large amounts of data by
detecting and asking the user to label only the most informative examples in
the domain. We focus here on active learning for multi-view domains, in which
there are several disjoint subsets of features (views), each of which is
sufficient to lear... | computer science |
31,870 | The Augmented Complex Kernel LMS | cs.LG | Recently, a unified framework for adaptive kernel based signal processing of
complex data was presented by the authors, which, besides offering techniques
to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a
suitable Wirtinger-like Calculus for general Hilbert Spaces. In this short
paper, the... | computer science |
31,871 | Dynamic Matrix Factorization: A State Space Approach | cs.LG | Matrix factorization from a small number of observed entries has recently
garnered much attention as the key ingredient of successful recommendation
systems. One unresolved problem in this area is how to adapt current methods to
handle changing user preferences over time. Recent proposals to address this
issue are heur... | computer science |
31,872 | Active Learning Using Smooth Relative Regret Approximations with
Applications | cs.LG | The disagreement coefficient of Hanneke has become a central data independent
invariant in proving active learning rates. It has been shown in various ways
that a concept class with low complexity together with a bound on the
disagreement coefficient at an optimal solution allows active learning rates
that are superior... | computer science |
31,873 | Supervised learning of short and high-dimensional temporal sequences for
life science measurements | cs.LG | The analysis of physiological processes over time are often given by
spectrometric or gene expression profiles over time with only few time points
but a large number of measured variables. The analysis of such temporal
sequences is challenging and only few methods have been proposed. The
information can be encoded time... | computer science |
31,874 | Dynamic Batch Bayesian Optimization | cs.LG | Bayesian optimization (BO) algorithms try to optimize an unknown function
that is expensive to evaluate using minimum number of evaluations/experiments.
Most of the proposed algorithms in BO are sequential, where only one experiment
is selected at each iteration. This method can be time inefficient when each
experiment... | computer science |
31,875 | Injecting External Solutions Into CMA-ES | cs.LG | This report considers how to inject external candidate solutions into the
CMA-ES algorithm. The injected solutions might stem from a gradient or a Newton
step, a surrogate model optimizer or any other oracle or search mechanism. They
can also be the result of a repair mechanism, for example to render infeasible
solutio... | computer science |
31,876 | Data-dependent kernels in nearly-linear time | cs.LG | We propose a method to efficiently construct data-dependent kernels which can
make use of large quantities of (unlabeled) data. Our construction makes an
approximation in the standard construction of semi-supervised kernels in
Sindhwani et al. 2005. In typical cases these kernels can be computed in
nearly-linear time (... | computer science |
31,877 | Learning Hierarchical and Topographic Dictionaries with Structured
Sparsity | cs.LG | Recent work in signal processing and statistics have focused on defining new
regularization functions, which not only induce sparsity of the solution, but
also take into account the structure of the problem. We present in this paper a
class of convex penalties introduced in the machine learning community, which
take th... | computer science |
31,878 | Wikipedia Edit Number Prediction based on Temporal Dynamics Only | cs.LG | In this paper, we describe our approach to the Wikipedia Participation
Challenge which aims to predict the number of edits a Wikipedia editor will
make in the next 5 months. The best submission from our team, "zeditor",
achieved 41.7% improvement over WMF's baseline predictive model and the final
rank of 3rd place amon... | computer science |
31,879 | Deciding of HMM parameters based on number of critical points for
gesture recognition from motion capture data | cs.LG | This paper presents a method of choosing number of states of a HMM based on
number of critical points of the motion capture data. The choice of Hidden
Markov Models(HMM) parameters is crucial for recognizer's performance as it is
the first step of the training and cannot be corrected automatically within
HMM. In this a... | computer science |
31,880 | PAC-Bayes-Bernstein Inequality for Martingales and its Application to
Multiarmed Bandits | cs.LG | We develop a new tool for data-dependent analysis of the
exploration-exploitation trade-off in learning under limited feedback. Our tool
is based on two main ingredients. The first ingredient is a new concentration
inequality that makes it possible to control the concentration of weighted
averages of multiple (possibly... | computer science |
31,881 | Confidence Estimation in Structured Prediction | cs.LG | Structured classification tasks such as sequence labeling and dependency
parsing have seen much interest by the Natural Language Processing and the
machine learning communities. Several online learning algorithms were adapted
for structured tasks such as Perceptron, Passive- Aggressive and the recently
introduced Confi... | computer science |
31,882 | Robust Interactive Learning | cs.LG | In this paper we propose and study a generalization of the standard
active-learning model where a more general type of query, class conditional
query, is allowed. Such queries have been quite useful in applications, but
have been lacking theoretical understanding. In this work, we characterize the
power of such queries... | computer science |
31,883 | Parametrized Stochastic Multi-armed Bandits with Binary Rewards | cs.LG | In this paper, we consider the problem of multi-armed bandits with a large,
possibly infinite number of correlated arms. We assume that the arms have
Bernoulli distributed rewards, independent across time, where the probabilities
of success are parametrized by known attribute vectors for each arm, as well as
an unknown... | computer science |
31,884 | Efficient Regression in Metric Spaces via Approximate Lipschitz
Extension | cs.LG | We present a framework for performing efficient regression in general metric
spaces. Roughly speaking, our regressor predicts the value at a new point by
computing a Lipschitz extension --- the smoothest function consistent with the
observed data --- after performing structural risk minimization to avoid
overfitting. W... | computer science |
31,885 | Large Scale Spectral Clustering Using Approximate Commute Time Embedding | cs.LG | Spectral clustering is a novel clustering method which can detect complex
shapes of data clusters. However, it requires the eigen decomposition of the
graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not
suitable for large scale systems. Recently, many methods have been proposed to
accelerate the com... | computer science |
31,886 | Trading Regret for Efficiency: Online Convex Optimization with Long Term
Constraints | cs.LG | In this paper we propose a framework for solving constrained online convex
optimization problem. Our motivation stems from the observation that most
algorithms proposed for online convex optimization require a projection onto
the convex set $\mathcal{K}$ from which the decisions are made. While for
simple shapes (e.g. ... | computer science |
31,887 | Regret Bound by Variation for Online Convex Optimization | cs.LG | In citep{Hazan-2008-extract}, the authors showed that the regret of online
linear optimization can be bounded by the total variation of the cost vectors.
In this paper, we extend this result to general online convex optimization. We
first analyze the limitations of the algorithm in \citep{Hazan-2008-extract}
when appli... | computer science |
31,888 | T-Learning | cs.LG | Traditional Reinforcement Learning (RL) has focused on problems involving
many states and few actions, such as simple grid worlds. Most real world
problems, however, are of the opposite type, Involving Few relevant states and
many actions. For example, to return home from a conference, humans identify
only few subgoal ... | computer science |
31,889 | A Topic Modeling Toolbox Using Belief Propagation | cs.LG | Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model
for probabilistic topic modeling, which attracts worldwide interests and
touches on many important applications in text mining, computer vision and
computational biology. This paper introduces a topic modeling toolbox (TMBP)
based on the beli... | computer science |
31,890 | Customers Behavior Modeling by Semi-Supervised Learning in Customer
Relationship Management | cs.LG | Leveraging the power of increasing amounts of data to analyze customer base
for attracting and retaining the most valuable customers is a major problem
facing companies in this information age. Data mining technologies extract
hidden information and knowledge from large data stored in databases or data
warehouses, ther... | computer science |
31,891 | Automatic Detection of Diabetes Diagnosis using Feature Weighted Support
Vector Machines based on Mutual Information and Modified Cuckoo Search | cs.LG | Diabetes is a major health problem in both developing and developed countries
and its incidence is rising dramatically. In this study, we investigate a novel
automatic approach to diagnose Diabetes disease based on Feature Weighted
Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The
proposed model c... | computer science |
31,892 | Stochastic Low-Rank Kernel Learning for Regression | cs.LG | We present a novel approach to learn a kernel-based regression function. It
is based on the useof conical combinations of data-based parameterized kernels
and on a new stochastic convex optimization procedure of which we establish
convergence guarantees. The overall learning procedure has the nice properties
that a) th... | computer science |
31,893 | Acoustical Quality Assessment of the Classroom Environment | cs.LG | Teaching is one of the most important factors affecting any education system.
Many research efforts have been conducted to facilitate the presentation modes
used by instructors in classrooms as well as provide means for students to
review lectures through web browsers. Other studies have been made to provide
acoustical... | computer science |
31,894 | An Efficient Primal-Dual Prox Method for Non-Smooth Optimization | cs.LG | We study the non-smooth optimization problems in machine learning, where both
the loss function and the regularizer are non-smooth functions. Previous
studies on efficient empirical loss minimization assume either a smooth loss
function or a strongly convex regularizer, making them unsuitable for
non-smooth optimizatio... | computer science |
31,895 | A Comparison Between Data Mining Prediction Algorithms for Fault
Detection(Case study: Ahanpishegan co.) | cs.LG | In the current competitive world, industrial companies seek to manufacture
products of higher quality which can be achieved by increasing reliability,
maintainability and thus the availability of products. On the other hand,
improvement in products lifecycle is necessary for achieving high reliability.
Typically, maint... | computer science |
31,896 | Active Learning of Custering with Side Information Using $\eps$-Smooth
Relative Regret Approximations | cs.LG | Clustering is considered a non-supervised learning setting, in which the goal
is to partition a collection of data points into disjoint clusters. Often a
bound $k$ on the number of clusters is given or assumed by the practitioner.
Many versions of this problem have been defined, most notably $k$-means and
$k$-median.
... | computer science |
31,897 | Application of Gist SVM in Cancer Detection | cs.LG | In this paper, we study the application of GIST SVM in disease prediction
(detection of cancer). Pattern classification problems can be effectively
solved by Support vector machines. Here we propose a classifier which can
differentiate patients having benign and malignant cancer cells. To improve the
accuracy of classi... | computer science |
31,898 | On the Necessity of Irrelevant Variables | cs.LG | This work explores the effects of relevant and irrelevant boolean variables
on the accuracy of classifiers. The analysis uses the assumption that the
variables are conditionally independent given the class, and focuses on a
natural family of learning algorithms for such sources when the relevant
variables have a small ... | computer science |
31,899 | Data Mining: A Prediction for Performance Improvement of Engineering
Students using Classification | cs.LG | Now-a-days the amount of data stored in educational database increasing
rapidly. These databases contain hidden information for improvement of
students' performance. Educational data mining is used to study the data
available in the educational field and bring out the hidden knowledge from it.
Classification methods li... | computer science |
31,900 | Adaptive Mixture Methods Based on Bregman Divergences | cs.LG | We investigate adaptive mixture methods that linearly combine outputs of $m$
constituent filters running in parallel to model a desired signal. We use
"Bregman divergences" and obtain certain multiplicative updates to train the
linear combination weights under an affine constraint or without any
constraints. We use unn... | computer science |
31,901 | Very Short Literature Survey From Supervised Learning To Surrogate
Modeling | cs.LG | The past century was era of linear systems. Either systems (especially
industrial ones) were simple (quasi)linear or linear approximations were
accurate enough. In addition, just at the ending decades of the century
profusion of computing devices were available, before then due to lack of
computational resources it was... | computer science |
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