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32,202 | Tangent Bundle Manifold Learning via Grassmann&Stiefel Eigenmaps | cs.LG | One of the ultimate goals of Manifold Learning (ML) is to reconstruct an
unknown nonlinear low-dimensional manifold embedded in a high-dimensional
observation space by a given set of data points from the manifold. We derive a
local lower bound for the maximum reconstruction error in a small neighborhood
of an arbitrary... | computer science |
32,203 | A game-theoretic framework for classifier ensembles using weighted
majority voting with local accuracy estimates | cs.LG | In this paper, a novel approach for the optimal combination of binary
classifiers is proposed. The classifier combination problem is approached from
a Game Theory perspective. The proposed framework of adapted weighted majority
rules (WMR) is tested against common rank-based, Bayesian and simple majority
models, as wel... | computer science |
32,204 | RandomBoost: Simplified Multi-class Boosting through Randomization | cs.LG | We propose a novel boosting approach to multi-class classification problems,
in which multiple classes are distinguished by a set of random projection
matrices in essence. The approach uses random projections to alleviate the
proliferation of binary classifiers typically required to perform multi-class
classification. ... | computer science |
32,205 | A Comparison of Relaxations of Multiset Cannonical Correlation Analysis
and Applications | cs.LG | Canonical correlation analysis is a statistical technique that is used to
find relations between two sets of variables. An important extension in pattern
analysis is to consider more than two sets of variables. This problem can be
expressed as a quadratically constrained quadratic program (QCQP), commonly
referred to M... | computer science |
32,206 | The price of bandit information in multiclass online classification | cs.LG | We consider two scenarios of multiclass online learning of a hypothesis class
$H\subseteq Y^X$. In the {\em full information} scenario, the learner is
exposed to instances together with their labels. In the {\em bandit} scenario,
the true label is not exposed, but rather an indication whether the learner's
prediction i... | computer science |
32,207 | Passive Learning with Target Risk | cs.LG | In this paper we consider learning in passive setting but with a slight
modification. We assume that the target expected loss, also referred to as
target risk, is provided in advance for learner as prior knowledge. Unlike most
studies in the learning theory that only incorporate the prior knowledge into
the generalizat... | computer science |
32,208 | Minimax Optimal Algorithms for Unconstrained Linear Optimization | cs.LG | We design and analyze minimax-optimal algorithms for online linear
optimization games where the player's choice is unconstrained. The player
strives to minimize regret, the difference between his loss and the loss of a
post-hoc benchmark strategy. The standard benchmark is the loss of the best
strategy chosen from a bo... | computer science |
32,209 | A Time Series Forest for Classification and Feature Extraction | cs.LG | We propose a tree ensemble method, referred to as time series forest (TSF),
for time series classification. TSF employs a combination of the entropy gain
and a distance measure, referred to as the Entrance (entropy and distance)
gain, for evaluating the splits. Experimental studies show that the Entrance
gain criterion... | computer science |
32,210 | Extracting useful rules through improved decision tree induction using
information entropy | cs.LG | Classification is widely used technique in the data mining domain, where
scalability and efficiency are the immediate problems in classification
algorithms for large databases. We suggest improvements to the existing C4.5
decision tree algorithm. In this paper attribute oriented induction (AOI) and
relevance analysis a... | computer science |
32,211 | Online Regret Bounds for Undiscounted Continuous Reinforcement Learning | cs.LG | We derive sublinear regret bounds for undiscounted reinforcement learning in
continuous state space. The proposed algorithm combines state aggregation with
the use of upper confidence bounds for implementing optimism in the face of
uncertainty. Beside the existence of an optimal policy which satisfies the
Poisson equat... | computer science |
32,212 | Selecting the State-Representation in Reinforcement Learning | cs.LG | The problem of selecting the right state-representation in a reinforcement
learning problem is considered. Several models (functions mapping past
observations to a finite set) of the observations are given, and it is known
that for at least one of these models the resulting state dynamics are indeed
Markovian. Without ... | computer science |
32,213 | Optimal Regret Bounds for Selecting the State Representation in
Reinforcement Learning | cs.LG | We consider an agent interacting with an environment in a single stream of
actions, observations, and rewards, with no reset. This process is not assumed
to be a Markov Decision Process (MDP). Rather, the agent has several
representations (mapping histories of past interactions to a discrete state
space) of the environ... | computer science |
32,214 | An Efficient Dual Approach to Distance Metric Learning | cs.LG | Distance metric learning is of fundamental interest in machine learning
because the distance metric employed can significantly affect the performance
of many learning methods. Quadratic Mahalanobis metric learning is a popular
approach to the problem, but typically requires solving a semidefinite
programming (SDP) prob... | computer science |
32,215 | Adaptive Crowdsourcing Algorithms for the Bandit Survey Problem | cs.LG | Very recently crowdsourcing has become the de facto platform for distributing
and collecting human computation for a wide range of tasks and applications
such as information retrieval, natural language processing and machine
learning. Current crowdsourcing platforms have some limitations in the area of
quality control.... | computer science |
32,216 | StructBoost: Boosting Methods for Predicting Structured Output Variables | cs.LG | Boosting is a method for learning a single accurate predictor by linearly
combining a set of less accurate weak learners. Recently, structured learning
has found many applications in computer vision. Inspired by structured support
vector machines (SSVM), here we propose a new boosting algorithm for structured
output pr... | computer science |
32,217 | Thompson Sampling in Switching Environments with Bayesian Online Change
Point Detection | cs.LG | Thompson Sampling has recently been shown to be optimal in the Bernoulli
Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes
stationary distributions for the rewards. It is often unrealistic to model the
real world as a stationary distribution. In this paper we derive and evaluate
algorithms ... | computer science |
32,218 | Graph-based Generalization Bounds for Learning Binary Relations | cs.LG | We investigate the generalizability of learned binary relations: functions
that map pairs of instances to a logical indicator. This problem has
application in numerous areas of machine learning, such as ranking, entity
resolution and link prediction. Our learning framework incorporates an example
labeler that, given a ... | computer science |
32,219 | The Importance of Clipping in Neurocontrol by Direct Gradient Descent on
the Cost-to-Go Function and in Adaptive Dynamic Programming | cs.LG | In adaptive dynamic programming, neurocontrol and reinforcement learning, the
objective is for an agent to learn to choose actions so as to minimise a total
cost function. In this paper we show that when discretized time is used to
model the motion of the agent, it can be very important to do "clipping" on the
motion o... | computer science |
32,220 | Prediction by Random-Walk Perturbation | cs.LG | We propose a version of the follow-the-perturbed-leader online prediction
algorithm in which the cumulative losses are perturbed by independent symmetric
random walks. The forecaster is shown to achieve an expected regret of the
optimal order O(sqrt(n log N)) where n is the time horizon and N is the number
of experts. ... | computer science |
32,221 | Sparse Frequency Analysis with Sparse-Derivative Instantaneous Amplitude
and Phase Functions | cs.LG | This paper addresses the problem of expressing a signal as a sum of frequency
components (sinusoids) wherein each sinusoid may exhibit abrupt changes in its
amplitude and/or phase. The Fourier transform of a narrow-band signal, with a
discontinuous amplitude and/or phase function, exhibits spectral and temporal
spreadi... | computer science |
32,222 | Online Learning for Time Series Prediction | cs.LG | In this paper we address the problem of predicting a time series using the
ARMA (autoregressive moving average) model, under minimal assumptions on the
noise terms. Using regret minimization techniques, we develop effective online
learning algorithms for the prediction problem, without assuming that the noise
terms are... | computer science |
32,223 | Online Convex Optimization Against Adversaries with Memory and
Application to Statistical Arbitrage | cs.LG | The framework of online learning with memory naturally captures learning
problems with temporal constraints, and was previously studied for the experts
setting. In this work we extend the notion of learning with memory to the
general Online Convex Optimization (OCO) framework, and present two algorithms
that attain low... | computer science |
32,224 | Online Similarity Prediction of Networked Data from Known and Unknown
Graphs | cs.LG | We consider online similarity prediction problems over networked data. We
begin by relating this task to the more standard class prediction problem,
showing that, given an arbitrary algorithm for class prediction, we can
construct an algorithm for similarity prediction with "nearly" the same mistake
bound, and vice ver... | computer science |
32,225 | Clustering Unclustered Data: Unsupervised Binary Labeling of Two
Datasets Having Different Class Balances | cs.LG | We consider the unsupervised learning problem of assigning labels to
unlabeled data. A naive approach is to use clustering methods, but this works
well only when data is properly clustered and each cluster corresponds to an
underlying class. In this paper, we first show that this unsupervised labeling
problem in balanc... | computer science |
32,226 | Perceptron Mistake Bounds | cs.LG | We present a brief survey of existing mistake bounds and introduce novel
bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds
generalize beyond standard margin-loss type bounds, allow for any convex and
Lipschitz loss function, and admit a very simple proof. | computer science |
32,227 | Deep Learning of Representations: Looking Forward | cs.LG | Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, se... | computer science |
32,228 | Spectral Classification Using Restricted Boltzmann Machine | cs.LG | In this study, a novel machine learning algorithm, restricted Boltzmann
machine (RBM), is introduced. The algorithm is applied for the spectral
classification in astronomy. RBM is a bipartite generative graphical model with
two separate layers (one visible layer and one hidden layer), which can extract
higher level fea... | computer science |
32,229 | Learning from Imprecise and Fuzzy Observations: Data Disambiguation
through Generalized Loss Minimization | cs.LG | Methods for analyzing or learning from "fuzzy data" have attracted increasing
attention in recent years. In many cases, however, existing methods (for
precise, non-fuzzy data) are extended to the fuzzy case in an ad-hoc manner,
and without carefully considering the interpretation of a fuzzy set when being
used for mode... | computer science |
32,230 | Simple Deep Random Model Ensemble | cs.LG | Representation learning and unsupervised learning are two central topics of
machine learning and signal processing. Deep learning is one of the most
effective unsupervised representation learning approach. The main contributions
of this paper to the topics are as follows. (i) We propose to view the
representative deep ... | computer science |
32,231 | A Differential Equations Approach to Optimizing Regret Trade-offs | cs.LG | We consider the classical question of predicting binary sequences and study
the {\em optimal} algorithms for obtaining the best possible regret and payoff
functions for this problem. The question turns out to be also equivalent to the
problem of optimal trade-offs between the regrets of two experts in an "experts
probl... | computer science |
32,232 | One-Pass AUC Optimization | cs.LG | AUC is an important performance measure and many algorithms have been devoted
to AUC optimization, mostly by minimizing a surrogate convex loss on a training
data set. In this work, we focus on one-pass AUC optimization that requires
only going through the training data once without storing the entire training
dataset,... | computer science |
32,233 | Class Imbalance Problem in Data Mining Review | cs.LG | In last few years there are major changes and evolution has been done on
classification of data. As the application area of technology is increases the
size of data also increases. Classification of data becomes difficult because
of unbounded size and imbalance nature of data. Class imbalance problem become
greatest is... | computer science |
32,234 | Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet
Allocation | cs.LG | In the internet era there has been an explosion in the amount of digital text
information available, leading to difficulties of scale for traditional
inference algorithms for topic models. Recent advances in stochastic
variational inference algorithms for latent Dirichlet allocation (LDA) have
made it feasible to learn... | computer science |
32,235 | An efficient algorithm for learning with semi-bandit feedback | cs.LG | We consider the problem of online combinatorial optimization under
semi-bandit feedback. The goal of the learner is to sequentially select its
actions from a combinatorial decision set so as to minimize its cumulative
loss. We propose a learning algorithm for this problem based on combining the
Follow-the-Perturbed-Lea... | computer science |
32,236 | Estimating or Propagating Gradients Through Stochastic Neurons | cs.LG | Stochastic neurons can be useful for a number of reasons in deep learning
models, but in many cases they pose a challenging problem: how to estimate the
gradient of a loss function with respect to the input of such stochastic
neurons, i.e., can we "back-propagate" through these stochastic neurons? We
examine this quest... | computer science |
32,237 | Contractive De-noising Auto-encoder | cs.LG | Auto-encoder is a special kind of neural network based on reconstruction.
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to
the input by corrupting the original data first and then reconstructing the
original input by minimizing the reconstruction error function. And contractive
auto-encoder ... | computer science |
32,238 | Ensembles of Classifiers based on Dimensionality Reduction | cs.LG | We present a novel approach for the construction of ensemble classifiers
based on dimensionality reduction. Dimensionality reduction methods represent
datasets using a small number of attributes while preserving the information
conveyed by the original dataset. The ensemble members are trained based on
dimension-reduce... | computer science |
32,239 | Generalized Denoising Auto-Encoders as Generative Models | cs.LG | Recent work has shown how denoising and contractive autoencoders implicitly
capture the structure of the data-generating density, in the case where the
corruption noise is Gaussian, the reconstruction error is the squared error,
and the data is continuous-valued. This has led to various proposals for
sampling from this... | computer science |
32,240 | Test cost and misclassification cost trade-off using reframing | cs.LG | Many solutions to cost-sensitive classification (and regression) rely on some
or all of the following assumptions: we have complete knowledge about the cost
context at training time, we can easily re-train whenever the cost context
changes, and we have technique-specific methods (such as cost-sensitive
decision trees) ... | computer science |
32,241 | Approximating the Bethe partition function | cs.LG | When belief propagation (BP) converges, it does so to a stationary point of
the Bethe free energy $F$, and is often strikingly accurate. However, it may
converge only to a local optimum or may not converge at all. An algorithm was
recently introduced for attractive binary pairwise MRFs which is guaranteed to
return an ... | computer science |
32,242 | Controlled Sparsity Kernel Learning | cs.LG | Multiple Kernel Learning(MKL) on Support Vector Machines(SVMs) has been a
popular front of research in recent times due to its success in application
problems like Object Categorization. This success is due to the fact that MKL
has the ability to choose from a variety of feature kernels to identify the
optimal kernel c... | computer science |
32,243 | EigenGP: Gaussian Process Models with Adaptive Eigenfunctions | cs.LG | Gaussian processes (GPs) provide a nonparametric representation of functions.
However, classical GP inference suffers from high computational cost for big
data. In this paper, we propose a new Bayesian approach, EigenGP, that learns
both basis dictionary elements--eigenfunctions of a GP prior--and prior
precisions in a... | computer science |
32,244 | Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits | cs.LG | Motivated by applications in energy management, this paper presents the
Multi-Armed Risk-Aware Bandit (MARAB) algorithm. With the goal of limiting the
exploration of risky arms, MARAB takes as arm quality its conditional value at
risk. When the user-supplied risk level goes to 0, the arm quality tends toward
the essent... | computer science |
32,245 | DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation | cs.LG | In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in seque... | computer science |
32,246 | Clustering, Coding, and the Concept of Similarity | cs.LG | This paper develops a theory of clustering and coding which combines a
geometric model with a probabilistic model in a principled way. The geometric
model is a Riemannian manifold with a Riemannian metric, ${g}_{ij}({\bf x})$,
which we interpret as a measure of dissimilarity. The probabilistic model
consists of a stoch... | computer science |
32,247 | Latent Tree Models and Approximate Inference in Bayesian Networks | cs.LG | We propose a novel method for approximate inference in Bayesian networks
(BNs). The idea is to sample data from a BN, learn a latent tree model (LTM)
from the data offline, and when online, make inference with the LTM instead of
the original BN. Because LTMs are tree-structured, inference takes linear time.
In the mean... | computer science |
32,248 | Adaptive Stochastic Resource Control: A Machine Learning Approach | cs.LG | The paper investigates stochastic resource allocation problems with scarce,
reusable resources and non-preemtive, time-dependent, interconnected tasks.
This approach is a natural generalization of several standard resource
management problems, such as scheduling and transportation problems. First,
reactive solutions ar... | computer science |
32,249 | Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based
Approach | cs.LG | Machine learning techniques are gaining prevalence in the production of a
wide range of classifiers for complex real-world applications with nonuniform
testing and misclassification costs. The increasing complexity of these
applications poses a real challenge to resource management during learning and
classification. I... | computer science |
32,250 | Regression Conformal Prediction with Nearest Neighbours | cs.LG | In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours
Regression (k-NNR) algorithm and propose ways of extending the typical
nonconformity measure used for regression so far. Unlike traditional regression
methods which produce point predictions, Conformal Predictors output predictive
regions that... | computer science |
32,251 | Convex Optimization for Binary Classifier Aggregation in Multiclass
Problems | cs.LG | Multiclass problems are often decomposed into multiple binary problems that
are solved by individual binary classifiers whose results are integrated into a
final answer. Various methods, including all-pairs (APs), one-versus-all (OVA),
and error correcting output code (ECOC), have been studied, to decompose
multiclass ... | computer science |
32,252 | A Unifying Framework for Typical Multi-Task Multiple Kernel Learning
Problems | cs.LG | Over the past few years, Multi-Kernel Learning (MKL) has received significant
attention among data-driven feature selection techniques in the context of
kernel-based learning. MKL formulations have been devised and solved for a
broad spectrum of machine learning problems, including Multi-Task Learning
(MTL). Solving di... | computer science |
32,253 | Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II) | cs.LG | An extreme learning machine (ELM) can be regarded as a two stage feed-forward
neural network (FNN) learning system which randomly assigns the connections
with and within hidden neurons in the first stage and tunes the connections
with output neurons in the second stage. Therefore, ELM training is essentially
a linear l... | computer science |
32,254 | Steady-state performance of non-negative least-mean-square algorithm and
its variants | cs.LG | Non-negative least-mean-square (NNLMS) algorithm and its variants have been
proposed for online estimation under non-negativity constraints. The transient
behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS
algorithms have been studied in our previous work. In this technical report, we
derive... | computer science |
32,255 | Riffled Independence for Efficient Inference with Partial Rankings | cs.LG | Distributions over rankings are used to model data in a multitude of real
world settings such as preference analysis and political elections. Modeling
such distributions presents several computational challenges, however, due to
the factorial size of the set of rankings over an item set. Some of these
challenges are qu... | computer science |
32,256 | Toward Supervised Anomaly Detection | cs.LG | Anomaly detection is being regarded as an unsupervised learning task as
anomalies stem from adversarial or unlikely events with unknown distributions.
However, the predictive performance of purely unsupervised anomaly detection
often fails to match the required detection rates in many tasks and there
exists a need for ... | computer science |
32,257 | A Lower Bound for the Variance of Estimators for Nakagami m Distribution | cs.LG | Recently, we have proposed a maximum likelihood iterative algorithm for
estimation of the parameters of the Nakagami-m distribution. This technique
performs better than state of art estimation techniques for this distribution.
This could be of particular use in low data or block based estimation problems.
In these scen... | computer science |
32,258 | A Feature Subset Selection Algorithm Automatic Recommendation Method | cs.LG | Many feature subset selection (FSS) algorithms have been proposed, but not
all of them are appropriate for a given feature selection problem. At the same
time, so far there is rarely a good way to choose appropriate FSS algorithms
for the problem at hand. Thus, FSS algorithm automatic recommendation is very
important a... | computer science |
32,259 | A Survey on Latent Tree Models and Applications | cs.LG | In data analysis, latent variables play a central role because they help
provide powerful insights into a wide variety of phenomena, ranging from
biological to human sciences. The latent tree model, a particular type of
probabilistic graphical models, deserves attention. Its simple structure - a
tree - allows simple an... | computer science |
32,260 | Near-Optimally Teaching the Crowd to Classify | cs.LG | How should we present training examples to learners to teach them
classification rules? This is a natural problem when training workers for
crowdsourcing labeling tasks, and is also motivated by challenges in
data-driven online education. We propose a natural stochastic model of the
learners, modeling them as randomly ... | computer science |
32,261 | Indian Buffet Process Deep Generative Models | cs.LG | Deep generative models (DGMs) have brought about a major breakthrough, as
well as renewed interest, in generative latent variable models. However, an
issue current DGM formulations do not address concerns the data-driven
inference of the number of latent features needed to represent the observed
data. Traditional linea... | computer science |
32,262 | Sparse Polynomial Learning and Graph Sketching | cs.LG | Let $f:\{-1,1\}^n$ be a polynomial with at most $s$ non-zero real
coefficients. We give an algorithm for exactly reconstructing f given random
examples from the uniform distribution on $\{-1,1\}^n$ that runs in time
polynomial in $n$ and $2s$ and succeeds if the function satisfies the unique
sign property: there is one... | computer science |
32,263 | Selective Sampling with Drift | cs.LG | Recently there has been much work on selective sampling, an online active
learning setting, in which algorithms work in rounds. On each round an
algorithm receives an input and makes a prediction. Then, it can decide whether
to query a label, and if so to update its model, otherwise the input is
discarded. Most of this... | computer science |
32,264 | On the properties of $α$-unchaining single linkage hierarchical
clustering | cs.LG | In the election of a hierarchical clustering method, theoretic properties may
give some insight to determine which method is the most suitable to treat a
clustering problem. Herein, we study some basic properties of two hierarchical
clustering methods: $\alpha$-unchaining single linkage or $SL(\alpha)$ and a
modified v... | computer science |
32,265 | Learning the Irreducible Representations of Commutative Lie Groups | cs.LG | We present a new probabilistic model of compact commutative Lie groups that
produces invariant-equivariant and disentangled representations of data. To
define the notion of disentangling, we borrow a fundamental principle from
physics that is used to derive the elementary particles of a system from its
symmetries. Our ... | computer science |
32,266 | A Survey on Semi-Supervised Learning Techniques | cs.LG | Semisupervised learning is a learning standard which deals with the study of
how computers and natural systems such as human beings acquire knowledge in the
presence of both labeled and unlabeled data. Semisupervised learning based
methods are preferred when compared to the supervised and unsupervised learning
because ... | computer science |
32,267 | A Quasi-Newton Method for Large Scale Support Vector Machines | cs.LG | This paper adapts a recently developed regularized stochastic version of the
Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton method for the
solution of support vector machine classification problems. The proposed method
is shown to converge almost surely to the optimal classifier at a rate that is
linear in... | computer science |
32,268 | To go deep or wide in learning? | cs.LG | To achieve acceptable performance for AI tasks, one can either use
sophisticated feature extraction methods as the first layer in a two-layered
supervised learning model, or learn the features directly using a deep
(multi-layered) model. While the first approach is very problem-specific, the
second approach has computa... | computer science |
32,269 | Bandits with concave rewards and convex knapsacks | cs.LG | In this paper, we consider a very general model for exploration-exploitation
tradeoff which allows arbitrary concave rewards and convex constraints on the
decisions across time, in addition to the customary limitation on the time
horizon. This model subsumes the classic multi-armed bandit (MAB) model, and
the Bandits w... | computer science |
32,270 | Renewable Energy Prediction using Weather Forecasts for Optimal
Scheduling in HPC Systems | cs.LG | The objective of the GreenPAD project is to use green energy (wind, solar and
biomass) for powering data-centers that are used to run HPC jobs. As a part of
this it is important to predict the Renewable (Wind) energy for efficient
scheduling (executing jobs that require higher energy when there is more green
energy ava... | computer science |
32,271 | Marginalizing Corrupted Features | cs.LG | The goal of machine learning is to develop predictors that generalize well to
test data. Ideally, this is achieved by training on an almost infinitely large
training data set that captures all variations in the data distribution. In
practical learning settings, however, we do not have infinite data and our
predictors m... | computer science |
32,272 | Exploiting the Statistics of Learning and Inference | cs.LG | When dealing with datasets containing a billion instances or with simulations
that require a supercomputer to execute, computational resources become part of
the equation. We can improve the efficiency of learning and inference by
exploiting their inherent statistical nature. We propose algorithms that
exploit the redu... | computer science |
32,273 | Sleep Analytics and Online Selective Anomaly Detection | cs.LG | We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to
model a specific scenario emerging from research in sleep science. Scientists
have segmented sleep into several stages and stage two is characterized by two
patterns (or anomalies) in the EEG time series recorded on sleep subjects.
These two ... | computer science |
32,274 | The Structurally Smoothed Graphlet Kernel | cs.LG | A commonly used paradigm for representing graphs is to use a vector that
contains normalized frequencies of occurrence of certain motifs or sub-graphs.
This vector representation can be used in a variety of applications, such as,
for computing similarity between graphs. The graphlet kernel of Shervashidze et
al. [32] u... | computer science |
32,275 | Unconstrained Online Linear Learning in Hilbert Spaces: Minimax
Algorithms and Normal Approximations | cs.LG | We study algorithms for online linear optimization in Hilbert spaces,
focusing on the case where the player is unconstrained. We develop a novel
characterization of a large class of minimax algorithms, recovering, and even
improving, several previous results as immediate corollaries. Moreover, using
our tools, we devel... | computer science |
32,276 | Integer Programming Relaxations for Integrated Clustering and Outlier
Detection | cs.LG | In this paper we present methods for exemplar based clustering with outlier
selection based on the facility location formulation. Given a distance function
and the number of outliers to be found, the methods automatically determine the
number of clusters and outliers. We formulate the problem as an integer program
to w... | computer science |
32,277 | Predictive Overlapping Co-Clustering | cs.LG | In the past few years co-clustering has emerged as an important data mining
tool for two way data analysis. Co-clustering is more advantageous over
traditional one dimensional clustering in many ways such as, ability to find
highly correlated sub-groups of rows and columns. However, one of the
overlooked benefits of co... | computer science |
32,278 | Improving Performance of a Group of Classification Algorithms Using
Resampling and Feature Selection | cs.LG | In recent years the importance of finding a meaningful pattern from huge
datasets has become more challenging. Data miners try to adopt innovative
methods to face this problem by applying feature selection methods. In this
paper we propose a new hybrid method in which we use a combination of
resampling, filtering the s... | computer science |
32,279 | Categorization Axioms for Clustering Results | cs.LG | Cluster analysis has attracted more and more attention in the field of
machine learning and data mining. Numerous clustering algorithms have been
proposed and are being developed due to diverse theories and various
requirements of emerging applications. Therefore, it is very worth establishing
an unified axiomatic fram... | computer science |
32,280 | A Hybrid Feature Selection Method to Improve Performance of a Group of
Classification Algorithms | cs.LG | In this paper a hybrid feature selection method is proposed which takes
advantages of wrapper subset evaluation with a lower cost and improves the
performance of a group of classifiers. The method uses combination of sample
domain filtering and resampling to refine the sample domain and two feature
subset evaluation me... | computer science |
32,281 | Cancer Prognosis Prediction Using Balanced Stratified Sampling | cs.LG | High accuracy in cancer prediction is important to improve the quality of the
treatment and to improve the rate of survivability of patients. As the data
volume is increasing rapidly in the healthcare research, the analytical
challenge exists in double. The use of effective sampling technique in
classification algorith... | computer science |
32,282 | A Survey of Algorithms and Analysis for Adaptive Online Learning | cs.LG | We present tools for the analysis of Follow-The-Regularized-Leader (FTRL),
Dual Averaging, and Mirror Descent algorithms when the regularizer
(equivalently, prox-function or learning rate schedule) is chosen adaptively
based on the data. Adaptivity can be used to prove regret bounds that hold on
every round, and also a... | computer science |
32,283 | Making Risk Minimization Tolerant to Label Noise | cs.LG | In many applications, the training data, from which one needs to learn a
classifier, is corrupted with label noise. Many standard algorithms such as SVM
perform poorly in presence of label noise. In this paper we investigate the
robustness of risk minimization to label noise. We prove a sufficient condition
on a loss f... | computer science |
32,284 | Mixed-norm Regularization for Brain Decoding | cs.LG | This work investigates the use of mixed-norm regularization for sensor
selection in Event-Related Potential (ERP) based Brain-Computer Interfaces
(BCI). The classification problem is cast as a discriminative optimization
framework where sensor selection is induced through the use of mixed-norms.
This framework is exten... | computer science |
32,285 | Learning Negative Mixture Models by Tensor Decompositions | cs.LG | This work considers the problem of estimating the parameters of negative
mixture models, i.e. mixture models that possibly involve negative weights. The
contributions of this paper are as follows. (i) We show that every rational
probability distributions on strings, a representation which occurs naturally
in spectral l... | computer science |
32,286 | Spectral Clustering with Jensen-type kernels and their multi-point
extensions | cs.LG | Motivated by multi-distribution divergences, which originate in information
theory, we propose a notion of `multi-point' kernels, and study their
applications. We study a class of kernels based on Jensen type divergences and
show that these can be extended to measure similarity among multiple points. We
study tensor fl... | computer science |
32,287 | Unconfused Ultraconservative Multiclass Algorithms | cs.LG | We tackle the problem of learning linear classifiers from noisy datasets in a
multiclass setting. The two-class version of this problem was studied a few
years ago by, e.g. Bylander (1994) and Blum et al. (1996): in these
contributions, the proposed approaches to fight the noise revolve around a
Perceptron learning sch... | computer science |
32,288 | Online Local Learning via Semidefinite Programming | cs.LG | In many online learning problems we are interested in predicting local
information about some universe of items. For example, we may want to know
whether two items are in the same cluster rather than computing an assignment
of items to clusters; we may want to know which of two teams will win a game
rather than computi... | computer science |
32,289 | An Information-Theoretic Analysis of Thompson Sampling | cs.LG | We provide an information-theoretic analysis of Thompson sampling that
applies across a broad range of online optimization problems in which a
decision-maker must learn from partial feedback. This analysis inherits the
simplicity and elegance of information theory and leads to regret bounds that
scale with the entropy ... | computer science |
32,290 | Learning to Optimize via Information-Directed Sampling | cs.LG | We propose information-directed sampling -- a new approach to online
optimization problems in which a decision-maker must balance between
exploration and exploitation while learning from partial feedback. Each action
is sampled in a manner that minimizes the ratio between squared expected
single-period regret and a mea... | computer science |
32,291 | Online Learning of k-CNF Boolean Functions | cs.LG | This paper revisits the problem of learning a k-CNF Boolean function from
examples in the context of online learning under the logarithmic loss. In doing
so, we give a Bayesian interpretation to one of Valiant's celebrated PAC
learning algorithms, which we then build upon to derive two efficient, online,
probabilistic,... | computer science |
32,292 | A study on cost behaviors of binary classification measures in
class-imbalanced problems | cs.LG | This work investigates into cost behaviors of binary classification measures
in a background of class-imbalanced problems. Twelve performance measures are
studied, such as F measure, G-means in terms of accuracy rates, and of recall
and precision, balance error rate (BER), Matthews correlation coefficient
(MCC), Kappa ... | computer science |
32,293 | Approximate Decentralized Bayesian Inference | cs.LG | This paper presents an approximate method for performing Bayesian inference
in models with conditional independence over a decentralized network of
learning agents. The method first employs variational inference on each
individual learning agent to generate a local approximate posterior, the agents
transmit their local... | computer science |
32,294 | On Exact Learning Monotone DNF from Membership Queries | cs.LG | In this paper, we study the problem of learning a monotone DNF with at most
$s$ terms of size (number of variables in each term) at most $r$ ($s$ term
$r$-MDNF) from membership queries. This problem is equivalent to the problem of
learning a general hypergraph using hyperedge-detecting queries, a problem
motivated by a... | computer science |
32,295 | Adaptation Algorithm and Theory Based on Generalized Discrepancy | cs.LG | We present a new algorithm for domain adaptation improving upon a discrepancy
minimization algorithm previously shown to outperform a number of algorithms
for this task. Unlike many previous algorithms for domain adaptation, our
algorithm does not consist of a fixed reweighting of the losses over the
training sample. W... | computer science |
32,296 | Learning Boolean Halfspaces with Small Weights from Membership Queries | cs.LG | We consider the problem of proper learning a Boolean Halfspace with integer
weights $\{0,1,\ldots,t\}$ from membership queries only. The best known
algorithm for this problem is an adaptive algorithm that asks $n^{O(t^5)}$
membership queries where the best lower bound for the number of membership
queries is $n^t$ [Lear... | computer science |
32,297 | Optimal Learners for Multiclass Problems | cs.LG | The fundamental theorem of statistical learning states that for binary
classification problems, any Empirical Risk Minimization (ERM) learning rule
has close to optimal sample complexity. In this paper we seek for a generic
optimal learner for multiclass prediction. We start by proving a surprising
result: a generic op... | computer science |
32,298 | A Canonical Semi-Deterministic Transducer | cs.LG | We prove the existence of a canonical form for semi-deterministic transducers
with incomparable sets of output strings. Based on this, we develop an
algorithm which learns semi-deterministic transducers given access to
translation queries. We also prove that there is no learning algorithm for
semi-deterministic transdu... | computer science |
32,299 | Selecting Near-Optimal Approximate State Representations in
Reinforcement Learning | cs.LG | We consider a reinforcement learning setting introduced in (Maillard et al.,
NIPS 2011) where the learner does not have explicit access to the states of the
underlying Markov decision process (MDP). Instead, she has access to several
models that map histories of past interactions to states. Here we improve over
known r... | computer science |
32,300 | Clustering, Hamming Embedding, Generalized LSH and the Max Norm | cs.LG | We study the convex relaxation of clustering and hamming embedding, focusing
on the asymmetric case (co-clustering and asymmetric hamming embedding),
understanding their relationship to LSH as studied by (Charikar 2002) and to
the max-norm ball, and the differences between their symmetric and asymmetric
versions. | computer science |
32,301 | Reducing Dueling Bandits to Cardinal Bandits | cs.LG | We present algorithms for reducing the Dueling Bandits problem to the
conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits
problem is an online model of learning with ordinal feedback of the form "A is
preferred to B" (as opposed to cardinal feedback like "A has value 2.5"),
giving it wide applica... | computer science |
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