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32,002 | Fast search for Dirichlet process mixture models | cs.LG | Dirichlet process (DP) mixture models provide a flexible Bayesian framework
for density estimation. Unfortunately, their flexibility comes at a cost:
inference in DP mixture models is computationally expensive, even when
conjugate distributions are used. In the common case when one seeks only a
maximum a posteriori ass... | computer science |
32,003 | Post-Processing of Discovered Association Rules Using Ontologies | cs.LG | In Data Mining, the usefulness of association rules is strongly limited by
the huge amount of delivered rules. In this paper we propose a new approach to
prune and filter discovered rules. Using Domain Ontologies, we strengthen the
integration of user knowledge in the post-processing task. Furthermore, an
interactive a... | computer science |
32,004 | Variable sigma Gaussian processes: An expectation propagation
perspective | cs.LG | Gaussian processes (GPs) provide a probabilistic nonparametric representation
of functions in regression, classification, and other problems. Unfortunately,
exact learning with GPs is intractable for large datasets. A variety of
approximate GP methods have been proposed that essentially map the large
dataset into a sma... | computer science |
32,005 | Effectiveness and Limitations of Statistical Spam Filters | cs.LG | In this paper we discuss the techniques involved in the design of the famous
statistical spam filters that include Naive Bayes, Term Frequency-Inverse
Document Frequency, K-Nearest Neighbor, Support Vector Machine, and Bayes
Additive Regression Tree. We compare these techniques with each other in terms
of accuracy, rec... | computer science |
32,006 | Competing with Gaussian linear experts | cs.LG | We study the problem of online regression. We prove a theoretical bound on
the square loss of Ridge Regression. We do not make any assumptions about input
vectors or outcomes. We also show that Bayesian Ridge Regression can be thought
of as an online algorithm competing with all the Gaussian linear experts. | computer science |
32,007 | Anomaly Detection with Score functions based on Nearest Neighbor Graphs | cs.LG | We propose a novel non-parametric adaptive anomaly detection algorithm for
high dimensional data based on score functions derived from nearest neighbor
graphs on $n$-point nominal data. Anomalies are declared whenever the score of
a test sample falls below $\alpha$, which is supposed to be the desired false
alarm level... | computer science |
32,008 | Optimal Query Complexity for Reconstructing Hypergraphs | cs.LG | In this paper we consider the problem of reconstructing a hidden weighted
hypergraph of constant rank using additive queries. We prove the following: Let
$G$ be a weighted hidden hypergraph of constant rank with n vertices and $m$
hyperedges. For any $m$ there exists a non-adaptive algorithm that finds the
edges of the... | computer science |
32,009 | Linear Probability Forecasting | cs.LG | Multi-class classification is one of the most important tasks in machine
learning. In this paper we consider two online multi-class classification
problems: classification by a linear model and by a kernelized model. The
quality of predictions is measured by the Brier loss function. We suggest two
computationally effic... | computer science |
32,010 | Measuring Latent Causal Structure | cs.LG | Discovering latent representations of the observed world has become
increasingly more relevant in data analysis. Much of the effort concentrates on
building latent variables which can be used in prediction problems, such as
classification and regression. A related goal of learning latent structure from
data is that of ... | computer science |
32,011 | Asymptotic Learning Curve and Renormalizable Condition in Statistical
Learning Theory | cs.LG | Bayes statistics and statistical physics have the common mathematical
structure, where the log likelihood function corresponds to the random
Hamiltonian. Recently, it was discovered that the asymptotic learning curves in
Bayes estimation are subject to a universal law, even if the log likelihood
function can not be app... | computer science |
32,012 | Role of Interestingness Measures in CAR Rule Ordering for Associative
Classifier: An Empirical Approach | cs.LG | Associative Classifier is a novel technique which is the integration of
Association Rule Mining and Classification. The difficult task in building
Associative Classifier model is the selection of relevant rules from a large
number of class association rules (CARs). A very popular method of ordering
rules for selection ... | computer science |
32,013 | Trajectory Clustering and an Application to Airspace Monitoring | cs.LG | This paper presents a framework aimed at monitoring the behavior of aircraft
in a given airspace. Nominal trajectories are determined and learned using data
driven methods. Standard procedures are used by air traffic controllers (ATC)
to guide aircraft, ensure the safety of the airspace, and to maximize the
runway occu... | computer science |
32,014 | Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop
MIMO Cooperative Systems | cs.LG | In this paper, we consider a queue-aware distributive resource control
algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay
buffering is an effective way to reduce the intrinsic half-duplex penalty in
cooperative systems. The complex interactions of the queues at the source node
and the relays... | computer science |
32,015 | Time Series Classification by Class-Specific Mahalanobis Distance
Measures | cs.LG | To classify time series by nearest neighbors, we need to specify or learn one
or several distance measures. We consider variations of the Mahalanobis
distance measures which rely on the inverse covariance matrix of the data.
Unfortunately --- for time series data --- the covariance matrix has often low
rank. To allevia... | computer science |
32,016 | Algorithms for nonnegative matrix factorization with the beta-divergence | cs.LG | This paper describes algorithms for nonnegative matrix factorization (NMF)
with the beta-divergence (beta-NMF). The beta-divergence is a family of cost
functions parametrized by a single shape parameter beta that takes the
Euclidean distance, the Kullback-Leibler divergence and the Itakura-Saito
divergence as special c... | computer science |
32,017 | Hardness Results for Agnostically Learning Low-Degree Polynomial
Threshold Functions | cs.LG | Hardness results for maximum agreement problems have close connections to
hardness results for proper learning in computational learning theory. In this
paper we prove two hardness results for the problem of finding a low degree
polynomial threshold function (PTF) which has the maximum possible agreement
with a given s... | computer science |
32,018 | Efficient Matrix Completion with Gaussian Models | cs.LG | A general framework based on Gaussian models and a MAP-EM algorithm is
introduced in this paper for solving matrix/table completion problems. The
numerical experiments with the standard and challenging movie ratings data show
that the proposed approach, based on probably one of the simplest probabilistic
models, leads ... | computer science |
32,019 | Large-Scale Clustering Based on Data Compression | cs.LG | This paper considers the clustering problem for large data sets. We propose
an approach based on distributed optimization. The clustering problem is
formulated as an optimization problem of maximizing the classification gain. We
show that the optimization problem can be reformulated and decomposed into
small-scale sub ... | computer science |
32,020 | Sublinear Optimization for Machine Learning | cs.LG | We give sublinear-time approximation algorithms for some optimization
problems arising in machine learning, such as training linear classifiers and
finding minimum enclosing balls. Our algorithms can be extended to some
kernelized versions of these problems, such as SVDD, hard margin SVM, and
L2-SVM, for which sublinea... | computer science |
32,021 | The Role of Normalization in the Belief Propagation Algorithm | cs.LG | An important part of problems in statistical physics and computer science can
be expressed as the computation of marginal probabilities over a Markov Random
Field. The belief propagation algorithm, which is an exact procedure to compute
these marginals when the underlying graph is a tree, has gained its popularity
as a... | computer science |
32,022 | Close the Gaps: A Learning-while-Doing Algorithm for a Class of
Single-Product Revenue Management Problems | cs.LG | We consider a retailer selling a single product with limited on-hand
inventory over a finite selling season. Customer demand arrives according to a
Poisson process, the rate of which is influenced by a single action taken by
the retailer (such as price adjustment, sales commission, advertisement
intensity, etc.). The r... | computer science |
32,023 | A Novel Template-Based Learning Model | cs.LG | This article presents a model which is capable of learning and abstracting
new concepts based on comparing observations and finding the resemblance
between the observations. In the model, the new observations are compared with
the templates which have been derived from the previous experiences. In the
first stage, the ... | computer science |
32,024 | Gaussian Robust Classification | cs.LG | Supervised learning is all about the ability to generalize knowledge.
Specifically, the goal of the learning is to train a classifier using training
data, in such a way that it will be capable of classifying new unseen data
correctly. In order to acheive this goal, it is important to carefully design
the learner, so it... | computer science |
32,025 | Meaningful Clustered Forest: an Automatic and Robust Clustering
Algorithm | cs.LG | We propose a new clustering technique that can be regarded as a numerical
method to compute the proximity gestalt. The method analyzes edge length
statistics in the MST of the dataset and provides an a contrario cluster
detection criterion. The approach is fully parametric on the chosen distance
and can detect arbitrar... | computer science |
32,026 | PAC learnability versus VC dimension: a footnote to a basic result of
statistical learning | cs.LG | A fundamental result of statistical learnig theory states that a concept
class is PAC learnable if and only if it is a uniform Glivenko-Cantelli class
if and only if the VC dimension of the class is finite. However, the theorem is
only valid under special assumptions of measurability of the class, in which
case the PAC... | computer science |
32,027 | Temporal Second Difference Traces | cs.LG | Q-learning is a reliable but inefficient off-policy temporal-difference
method, backing up reward only one step at a time. Replacing traces, using a
recency heuristic, are more efficient but less reliable. In this work, we
introduce model-free, off-policy temporal difference methods that make better
use of experience t... | computer science |
32,028 | Reducing Commitment to Tasks with Off-Policy Hierarchical Reinforcement
Learning | cs.LG | In experimenting with off-policy temporal difference (TD) methods in
hierarchical reinforcement learning (HRL) systems, we have observed unwanted
on-policy learning under reproducible conditions. Here we present modifications
to several TD methods that prevent unintentional on-policy learning from
occurring. These modi... | computer science |
32,029 | Attacking and Defending Covert Channels and Behavioral Models | cs.LG | In this paper we present methods for attacking and defending $k$-gram
statistical analysis techniques that are used, for example, in network traffic
analysis and covert channel detection. The main new result is our demonstration
of how to use a behavior's or process' $k$-order statistics to build a
stochastic process t... | computer science |
32,030 | Memory Constraint Online Multitask Classification | cs.LG | We investigate online kernel algorithms which simultaneously process multiple
classification tasks while a fixed constraint is imposed on the size of their
active sets. We focus in particular on the design of algorithms that can
efficiently deal with problems where the number of tasks is extremely high and
the task dat... | computer science |
32,031 | TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data
Classification | cs.LG | We introduce semi-supervised data classification algorithms based on total
variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine
(SVM), Cheeger cut, labeled and unlabeled data points. We design binary and
multi-class semi-supervised classification algorithms. We compare the TV-based
classifica... | computer science |
32,032 | Fast Online EM for Big Topic Modeling | cs.LG | The expectation-maximization (EM) algorithm can compute the
maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the
mixture models or latent variable models such as latent Dirichlet allocation
(LDA), which has been one of the most popular probabilistic topic modeling
methods in the past decade. Howev... | computer science |
32,033 | Blending Learning and Inference in Structured Prediction | cs.LG | In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machine... | computer science |
32,034 | A Direct Approach to Multi-class Boosting and Extensions | cs.LG | Boosting methods combine a set of moderately accurate weaklearners to form a
highly accurate predictor. Despite the practical importance of multi-class
boosting, it has received far less attention than its binary counterpart. In
this work, we propose a fully-corrective multi-class boosting formulation which
directly so... | computer science |
32,035 | Bayesian Estimation for Continuous-Time Sparse Stochastic Processes | cs.LG | We consider continuous-time sparse stochastic processes from which we have
only a finite number of noisy/noiseless samples. Our goal is to estimate the
noiseless samples (denoising) and the signal in-between (interpolation
problem).
By relying on tools from the theory of splines, we derive the joint a priori
distribu... | computer science |
32,036 | Online Learning in Decentralized Multiuser Resource Sharing Problems | cs.LG | In this paper, we consider the general scenario of resource sharing in a
decentralized system when the resource rewards/qualities are time-varying and
unknown to the users, and using the same resource by multiple users leads to
reduced quality due to resource sharing. Firstly, we consider a
user-independent reward mode... | computer science |
32,037 | A density-sensitive hierarchical clustering method | cs.LG | We define a hierarchical clustering method: $\alpha$-unchaining single
linkage or $SL(\alpha)$. The input of this algorithm is a finite metric space
and a certain parameter $\alpha$. This method is sensitive to the density of
the distribution and offers some solution to the so called chaining effect. We
also define a m... | computer science |
32,038 | Text Classification with Compression Algorithms | cs.LG | This work concerns a comparison of SVM kernel methods in text categorization
tasks. In particular I define a kernel function that estimates the similarity
between two objects computing by their compressed lengths. In fact, compression
algorithms can detect arbitrarily long dependencies within the text strings.
Data tex... | computer science |
32,039 | Holistic Measures for Evaluating Prediction Models in Smart Grids | cs.LG | The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance m... | computer science |
32,040 | Learning the Information Divergence | cs.LG | Information divergence that measures the difference between two nonnegative
matrices or tensors has found its use in a variety of machine learning
problems. Examples are Nonnegative Matrix/Tensor Factorization, Stochastic
Neighbor Embedding, topic models, and Bayesian network optimization. The
success of such a learnin... | computer science |
32,041 | Learning to Discover Efficient Mathematical Identities | cs.LG | In this paper we explore how machine learning techniques can be applied to
the discovery of efficient mathematical identities. We introduce an attribute
grammar framework for representing symbolic expressions. Given a set of grammar
rules we build trees that combine different rules, looking for branches which
yield com... | computer science |
32,042 | Logarithmic Time Online Multiclass prediction | cs.LG | We study the problem of multiclass classification with an extremely large
number of classes (k), with the goal of obtaining train and test time
complexity logarithmic in the number of classes. We develop top-down tree
construction approaches for constructing logarithmic depth trees. On the
theoretical front, we formula... | computer science |
32,043 | A Credit Assignment Compiler for Joint Prediction | cs.LG | Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, impl... | computer science |
32,044 | A Drifting-Games Analysis for Online Learning and Applications to
Boosting | cs.LG | We provide a general mechanism to design online learning algorithms based on
a minimax analysis within a drifting-games framework. Different online learning
settings (Hedge, multi-armed bandit problems and online convex optimization)
are studied by converting into various kinds of drifting games. The original
minimax a... | computer science |
32,045 | Reweighted Wake-Sleep | cs.LG | Training deep directed graphical models with many hidden variables and
performing inference remains a major challenge. Helmholtz machines and deep
belief networks are such models, and the wake-sleep algorithm has been proposed
to train them. The wake-sleep algorithm relies on training not just the
directed generative m... | computer science |
32,046 | Kalman Temporal Differences | cs.LG | Because reinforcement learning suffers from a lack of scalability, online
value (and Q-) function approximation has received increasing interest this
last decade. This contribution introduces a novel approximation scheme, namely
the Kalman Temporal Differences (KTD) framework, that exhibits the following
features: samp... | computer science |
32,047 | Restricted Boltzmann Machine for Classification with Hierarchical
Correlated Prior | cs.LG | Restricted Boltzmann machines (RBM) and its variants have become hot research
topics recently, and widely applied to many classification problems, such as
character recognition and document categorization. Often, classification RBM
ignores the interclass relationship or prior knowledge of sharing information
among clas... | computer science |
32,048 | Evaluation of Machine Learning Techniques for Green Energy Prediction | cs.LG | We evaluate the following Machine Learning techniques for Green Energy (Wind,
Solar) Prediction: Bayesian Inference, Neural Networks, Support Vector
Machines, Clustering techniques (PCA). Our objective is to predict green energy
using weather forecasts, predict deviations from forecast green energy, find
correlation am... | computer science |
32,049 | Optimal Resource Allocation with Semi-Bandit Feedback | cs.LG | We study a sequential resource allocation problem involving a fixed number of
recurring jobs. At each time-step the manager should distribute available
resources among the jobs in order to maximise the expected number of completed
jobs. Allocating more resources to a given job increases the probability that
it complete... | computer science |
32,050 | A Sober Look at Spectral Learning | cs.LG | Spectral learning recently generated lots of excitement in machine learning,
largely because it is the first known method to produce consistent estimates
(under suitable conditions) for several latent variable models. In contrast,
maximum likelihood estimates may get trapped in local optima due to the
non-convex nature... | computer science |
32,051 | An Experimental Evaluation of Nearest Neighbour Time Series
Classification | cs.LG | Data mining research into time series classification (TSC) has focussed on
alternative distance measures for nearest neighbour classifiers. It is standard
practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a
straw man for comparison. As part of a wider investigation into elastic
distance meas... | computer science |
32,052 | Learning computationally efficient dictionaries and their implementation
as fast transforms | cs.LG | Dictionary learning is a branch of signal processing and machine learning
that aims at finding a frame (called dictionary) in which some training data
admits a sparse representation. The sparser the representation, the better the
dictionary. The resulting dictionary is in general a dense matrix, and its
manipulation ca... | computer science |
32,053 | From conformal to probabilistic prediction | cs.LG | This paper proposes a new method of probabilistic prediction, which is based
on conformal prediction. The method is applied to the standard USPS data set
and gives encouraging results. | computer science |
32,054 | SPSD Matrix Approximation vis Column Selection: Theories, Algorithms,
and Extensions | cs.LG | Symmetric positive semidefinite (SPSD) matrix approximation is an important
problem with applications in kernel methods. However, existing SPSD matrix
approximation methods such as the Nystr\"om method only have weak error bounds.
In this paper we conduct in-depth studies of an SPSD matrix approximation model
and estab... | computer science |
32,055 | Stationary Mixing Bandits | cs.LG | We study the bandit problem where arms are associated with stationary
phi-mixing processes and where rewards are therefore dependent: the question
that arises from this setting is that of recovering some independence by
ignoring the value of some rewards. As we shall see, the bandit problem we
tackle requires us to add... | computer science |
32,056 | Mining Recurrent Concepts in Data Streams using the Discrete Fourier
Transform | cs.LG | In this research we address the problem of capturing recurring concepts in a
data stream environment. Recurrence capture enables the re-use of previously
learned classifiers without the need for re-learning while providing for better
accuracy during the concept recurrence interval. We capture concepts by
applying the D... | computer science |
32,057 | Generalized Mixability via Entropic Duality | cs.LG | Mixability is a property of a loss which characterizes when fast convergence
is possible in the game of prediction with expert advice. We show that a key
property of mixability generalizes, and the exp and log operations present in
the usual theory are not as special as one might have thought. In doing this we
introduc... | computer science |
32,058 | Composite Likelihood Estimation for Restricted Boltzmann machines | cs.LG | Learning the parameters of graphical models using the maximum likelihood
estimation is generally hard which requires an approximation. Maximum composite
likelihood estimations are statistical approximations of the maximum likelihood
estimation which are higher-order generalizations of the maximum
pseudo-likelihood esti... | computer science |
32,059 | Incremental Clustering: The Case for Extra Clusters | cs.LG | The explosion in the amount of data available for analysis often necessitates
a transition from batch to incremental clustering methods, which process one
element at a time and typically store only a small subset of the data. In this
paper, we initiate the formal analysis of incremental clustering methods
focusing on t... | computer science |
32,060 | Comparison of SVM Optimization Techniques in the Primal | cs.LG | This paper examines the efficacy of different optimization techniques in a
primal formulation of a support vector machine (SVM). Three main techniques are
compared. The dataset used to compare all three techniques was the Sentiment
Analysis on Movie Reviews dataset, from kaggle.com. | computer science |
32,061 | Contrastive Feature Induction for Efficient Structure Learning of
Conditional Random Fields | cs.LG | Structure learning of Conditional Random Fields (CRFs) can be cast into an
L1-regularized optimization problem. To avoid optimizing over a fully linked
model, gain-based or gradient-based feature selection methods start from an
empty model and incrementally add top ranked features to it. However, for
high-dimensional p... | computer science |
32,062 | Unimodal Bandits without Smoothness | cs.LG | We consider stochastic bandit problems with a continuous set of arms and
where the expected reward is a continuous and unimodal function of the arm. No
further assumption is made regarding the smoothness and the structure of the
expected reward function. For these problems, we propose the Stochastic
Pentachotomy (SP) a... | computer science |
32,063 | Local Component Analysis | cs.LG | Kernel density estimation, a.k.a. Parzen windows, is a popular density
estimation method, which can be used for outlier detection or clustering. With
multivariate data, its performance is heavily reliant on the metric used within
the kernel. Most earlier work has focused on learning only the bandwidth of the
kernel (i.... | computer science |
32,064 | Weighted Clustering | cs.LG | One of the most prominent challenges in clustering is "the user's dilemma,"
which is the problem of selecting an appropriate clustering algorithm for a
specific task. A formal approach for addressing this problem relies on the
identification of succinct, user-friendly properties that formally capture when
certain clust... | computer science |
32,065 | Learning From Labeled And Unlabeled Data: An Empirical Study Across
Techniques And Domains | cs.LG | There has been increased interest in devising learning techniques that
combine unlabeled data with labeled data ? i.e. semi-supervised learning.
However, to the best of our knowledge, no study has been performed across
various techniques and different types and amounts of labeled and unlabeled
data. Moreover, most of t... | computer science |
32,066 | Efficiency versus Convergence of Boolean Kernels for On-Line Learning
Algorithms | cs.LG | The paper studies machine learning problems where each example is described
using a set of Boolean features and where hypotheses are represented by linear
threshold elements. One method of increasing the expressiveness of learned
hypotheses in this context is to expand the feature set to include conjunctions
of basic f... | computer science |
32,067 | Risk-Sensitive Reinforcement Learning Applied to Control under
Constraints | cs.LG | In this paper, we consider Markov Decision Processes (MDPs) with error
states. Error states are those states entering which is undesirable or
dangerous. We define the risk with respect to a policy as the probability of
entering such a state when the policy is pursued. We consider the problem of
finding good policies wh... | computer science |
32,068 | Bandits with an Edge | cs.LG | We consider a bandit problem over a graph where the rewards are not directly
observed. Instead, the decision maker can compare two nodes and receive
(stochastic) information pertaining to the difference in their value. The graph
structure describes the set of possible comparisons. Consequently, comparing
between two no... | computer science |
32,069 | Distributed User Profiling via Spectral Methods | cs.LG | User profiling is a useful primitive for constructing personalised services,
such as content recommendation. In the present paper we investigate the
feasibility of user profiling in a distributed setting, with no central
authority and only local information exchanges between users. We compute a
profile vector for each ... | computer science |
32,070 | Learning Topic Models by 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 represents LDA as a factor graph within the
Markov random fi... | computer science |
32,071 | Application of distances between terms for flat and hierarchical data | cs.LG | In machine learning, distance-based algorithms, and other approaches, use
information that is represented by propositional data. However, this kind of
representation can be quite restrictive and, in many cases, it requires more
complex structures in order to represent data in a more natural way. Terms are
the basis for... | computer science |
32,072 | Noise Tolerance under Risk Minimization | cs.LG | In this paper we explore noise tolerant learning of classifiers. We formulate
the problem as follows. We assume that there is an ${\bf unobservable}$
training set which is noise-free. The actual training set given to the learning
algorithm is obtained from this ideal data set by corrupting the class label of
each examp... | computer science |
32,073 | Minimax Classifier for Uncertain Costs | cs.LG | Many studies on the cost-sensitive learning assumed that a unique cost matrix
is known for a problem. However, this assumption may not hold for many
real-world problems. For example, a classifier might need to be applied in
several circumstances, each of which associates with a different cost matrix.
Or, different huma... | computer science |
32,074 | Greedy Multiple Instance Learning via Codebook Learning and Nearest
Neighbor Voting | cs.LG | Multiple instance learning (MIL) has attracted great attention recently in
machine learning community. However, most MIL algorithms are very slow and
cannot be applied to large datasets. In this paper, we propose a greedy
strategy to speed up the multiple instance learning process. Our contribution
is two fold. First, ... | computer science |
32,075 | A Converged Algorithm for Tikhonov Regularized Nonnegative Matrix
Factorization with Automatic Regularization Parameters Determination | cs.LG | We present a converged algorithm for Tikhonov regularized nonnegative matrix
factorization (NMF). We specially choose this regularization because it is
known that Tikhonov regularized least square (LS) is the more preferable form
in solving linear inverse problems than the conventional LS. Because an NMF
problem can be... | computer science |
32,076 | Efficient Constrained Regret Minimization | cs.LG | Online learning constitutes a mathematical and compelling framework to
analyze sequential decision making problems in adversarial environments. The
learner repeatedly chooses an action, the environment responds with an outcome,
and then the learner receives a reward for the played action. The goal of the
learner is to ... | computer science |
32,077 | A Uniqueness Theorem for Clustering | cs.LG | Despite the widespread use of Clustering, there is distressingly little
general theory of clustering available. Questions like "What distinguishes a
clustering of data from other data partitioning?", "Are there any principles
governing all clustering paradigms?", "How should a user choose an appropriate
clustering algo... | computer science |
32,078 | The Entire Quantile Path of a Risk-Agnostic SVM Classifier | cs.LG | A quantile binary classifier uses the rule: Classify x as +1 if P(Y = 1|X =
x) >= t, and as -1 otherwise, for a fixed quantile parameter t {[0, 1]. It has
been shown that Support Vector Machines (SVMs) in the limit are quantile
classifiers with t = 1/2 . In this paper, we show that by using asymmetric cost
of misclassi... | computer science |
32,079 | Probabilistic Structured Predictors | cs.LG | We consider MAP estimators for structured prediction with exponential family
models. In particular, we concentrate on the case that efficient algorithms for
uniform sampling from the output space exist. We show that under this
assumption (i) exact computation of the partition function remains a hard
problem, and (ii) t... | computer science |
32,080 | REGAL: A Regularization based Algorithm for Reinforcement Learning in
Weakly Communicating MDPs | cs.LG | We provide an algorithm that achieves the optimal regret rate in an unknown
weakly communicating Markov Decision Process (MDP). The algorithm proceeds in
episodes where, in each episode, it picks a policy using regularization based
on the span of the optimal bias vector. For an MDP with S states and A actions
whose opt... | computer science |
32,081 | A Bayesian Sampling Approach to Exploration in Reinforcement Learning | cs.LG | We present a modular approach to reinforcement learning that uses a Bayesian
representation of the uncertainty over models. The approach, BOSS (Best of
Sampled Set), drives exploration by sampling multiple models from the posterior
and selecting actions optimistically. It extends previous work by providing a
rule for d... | computer science |
32,082 | Decoupling Exploration and Exploitation in Multi-Armed Bandits | cs.LG | We consider a multi-armed bandit problem where the decision maker can explore
and exploit different arms at every round. The exploited arm adds to the
decision maker's cumulative reward (without necessarily observing the reward)
while the explored arm reveals its value. We devise algorithms for this setup
and show that... | computer science |
32,083 | Normalized Maximum Likelihood Coding for Exponential Family with Its
Applications to Optimal Clustering | cs.LG | We are concerned with the issue of how to calculate the normalized maximum
likelihood (NML) code-length. There is a problem that the normalization term of
the NML code-length may diverge when it is continuous and unbounded and a
straightforward computation of it is highly expensive when the data domain is
finite . In p... | computer science |
32,084 | Visualization of features of a series of measurements with
one-dimensional cellular structure | cs.LG | This paper describes the method of visualization of periodic constituents and
instability areas in series of measurements, being based on the algorithm of
smoothing out and concept of one-dimensional cellular automata. A method can be
used at the analysis of temporal series, related to the volumes of thematic
publicati... | computer science |
32,085 | The Role of Weight Shrinking in Large Margin Perceptron Learning | cs.LG | We introduce into the classical perceptron algorithm with margin a mechanism
that shrinks the current weight vector as a first step of the update. If the
shrinking factor is constant the resulting algorithm may be regarded as a
margin-error-driven version of NORMA with constant learning rate. In this case
we show that ... | computer science |
32,086 | Safe Exploration in Markov Decision Processes | cs.LG | In environments with uncertain dynamics exploration is necessary to learn how
to perform well. Existing reinforcement learning algorithms provide strong
exploration guarantees, but they tend to rely on an ergodicity assumption. The
essence of ergodicity is that any state is eventually reachable from any other
state by ... | computer science |
32,087 | Off-Policy Actor-Critic | cs.LG | This paper presents the first actor-critic algorithm for off-policy
reinforcement learning. Our algorithm is online and incremental, and its
per-time-step complexity scales linearly with the number of learned weights.
Previous work on actor-critic algorithms is limited to the on-policy setting
and does not take advanta... | computer science |
32,088 | Multiclass Learning Approaches: A Theoretical Comparison with
Implications | cs.LG | We theoretically analyze and compare the following five popular multiclass
classification methods: One vs. All, All Pairs, Tree-based classifiers, Error
Correcting Output Codes (ECOC) with randomly generated code matrices, and
Multiclass SVM. In the first four methods, the classification is based on a
reduction to bina... | computer science |
32,089 | On Multilabel Classification and Ranking with Partial Feedback | cs.LG | We present a novel multilabel/ranking algorithm working in partial
information settings. The algorithm is based on 2nd-order descent methods, and
relies on upper-confidence bounds to trade-off exploration and exploitation. We
analyze this algorithm in a partial adversarial setting, where covariates can
be adversarial, ... | computer science |
32,090 | Hybrid Template Update System for Unimodal Biometric Systems | cs.LG | Semi-supervised template update systems allow to automatically take into
account the intra-class variability of the biometric data over time. Such
systems can be inefficient by including too many impostor's samples or skipping
too many genuine's samples. In the first case, the biometric reference drifts
from the real b... | computer science |
32,091 | Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A
Statistical Analysis | cs.LG | Most keystroke dynamics studies have been evaluated using a specific kind of
dataset in which users type an imposed login and password. Moreover, these
studies are optimistics since most of them use different acquisition protocols,
private datasets, controlled environment, etc. In order to enhance the accuracy
of keyst... | computer science |
32,092 | Accuracy Measures for the Comparison of Classifiers | cs.LG | The selection of the best classification algorithm for a given dataset is a
very widespread problem. It is also a complex one, in the sense it requires to
make several important methodological choices. Among them, in this work we
focus on the measure used to assess the classification performance and rank the
algorithms... | computer science |
32,093 | Better Mixing via Deep Representations | cs.LG | It has previously been hypothesized, and supported with some experimental
evidence, that deeper representations, when well trained, tend to do a better
job at disentangling the underlying factors of variation. We study the
following related conjecture: better representations, in the sense of better
disentangling, can b... | computer science |
32,094 | Supervised Laplacian Eigenmaps with Applications in Clinical Diagnostics
for Pediatric Cardiology | cs.LG | Electronic health records contain rich textual data which possess critical
predictive information for machine-learning based diagnostic aids. However many
traditional machine learning methods fail to simultaneously integrate both
vector space data and text. We present a supervised method using Laplacian
eigenmaps to au... | computer science |
32,095 | Learning Hash Functions Using Column Generation | cs.LG | Fast nearest neighbor searching is becoming an increasingly important tool in
solving many large-scale problems. Recently a number of approaches to learning
data-dependent hash functions have been developed. In this work, we propose a
column generation based method for learning data-dependent hash functions on
the basi... | computer science |
32,096 | Inductive Sparse Subspace Clustering | cs.LG | Sparse Subspace Clustering (SSC) has achieved state-of-the-art clustering
quality by performing spectral clustering over a $\ell^{1}$-norm based
similarity graph. However, SSC is a transductive method which does not handle
with the data not used to construct the graph (out-of-sample data). For each
new datum, SSC requi... | computer science |
32,097 | Convex and Scalable Weakly Labeled SVMs | cs.LG | In this paper, we study the problem of learning from weakly labeled data,
where labels of the training examples are incomplete. This includes, for
example, (i) semi-supervised learning where labels are partially known; (ii)
multi-instance learning where labels are implicitly known; and (iii) clustering
where labels are... | computer science |
32,098 | Multi-relational Learning Using Weighted Tensor Decomposition with
Modular Loss | cs.LG | We propose a modular framework for multi-relational learning via tensor
decomposition. In our learning setting, the training data contains multiple
types of relationships among a set of objects, which we represent by a sparse
three-mode tensor. The goal is to predict the values of the missing entries. To
do so, we mode... | computer science |
32,099 | Transfer Learning for Voice Activity Detection: A Denoising Deep Neural
Network Perspective | cs.LG | Mismatching problem between the source and target noisy corpora severely
hinder the practical use of the machine-learning-based voice activity detection
(VAD). In this paper, we try to address this problem in the transfer learning
prospective. Transfer learning tries to find a common learning machine or a
common featur... | computer science |
32,100 | Convex Discriminative Multitask Clustering | cs.LG | Multitask clustering tries to improve the clustering performance of multiple
tasks simultaneously by taking their relationship into account. Most existing
multitask clustering algorithms fall into the type of generative clustering,
and none are formulated as convex optimization problems. In this paper, we
propose two c... | computer science |
32,101 | Heuristic Ternary Error-Correcting Output Codes Via Weight Optimization
and Layered Clustering-Based Approach | cs.LG | One important classifier ensemble for multiclass classification problems is
Error-Correcting Output Codes (ECOCs). It bridges multiclass problems and
binary-class classifiers by decomposing multiclass problems to a serial
binary-class problems. In this paper, we present a heuristic ternary code,
named Weight Optimizati... | computer science |
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