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31,902 | Credal Classification based on AODE and compression coefficients | cs.LG | Bayesian model averaging (BMA) is an approach to average over alternative
models; yet, it usually gets excessively concentrated around the single most
probable model, therefore achieving only sub-optimal classification
performance. The compression-based approach (Boulle, 2007) overcomes this
problem, averaging over the... | computer science |
31,903 | The Kernelized Stochastic Batch Perceptron | cs.LG | We present a novel approach for training kernel Support Vector Machines,
establish learning runtime guarantees for our method that are better then those
of any other known kernelized SVM optimization approach, and show that our
method works well in practice compared to existing alternatives. | computer science |
31,904 | Stochastic Feature Mapping for PAC-Bayes Classification | cs.LG | Probabilistic generative modeling of data distributions can potentially
exploit hidden information which is useful for discriminative classification.
This observation has motivated the development of approaches that couple
generative and discriminative models for classification. In this paper, we
propose a new approach... | computer science |
31,905 | Supervised feature evaluation by consistency analysis: application to
measure sets used to characterise geographic objects | cs.LG | Nowadays, supervised learning is commonly used in many domains. Indeed, many
works propose to learn new knowledge from examples that translate the expected
behaviour of the considered system. A key issue of supervised learning concerns
the description language used to represent the examples. In this paper, we
propose a... | computer science |
31,906 | APRIL: Active Preference-learning based Reinforcement Learning | cs.LG | This paper focuses on reinforcement learning (RL) with limited prior
knowledge. In the domain of swarm robotics for instance, the expert can hardly
design a reward function or demonstrate the target behavior, forbidding the use
of both standard RL and inverse reinforcement learning. Although with a limited
expertise, t... | computer science |
31,907 | Data Selection for Semi-Supervised Learning | cs.LG | The real challenge in pattern recognition task and machine learning process
is to train a discriminator using labeled data and use it to distinguish
between future data as accurate as possible. However, most of the problems in
the real world have numerous data, which labeling them is a cumbersome or even
an impossible ... | computer science |
31,908 | Guess Who Rated This Movie: Identifying Users Through Subspace
Clustering | cs.LG | It is often the case that, within an online recommender system, multiple
users share a common account. Can such shared accounts be identified solely on
the basis of the user- provided ratings? Once a shared account is identified,
can the different users sharing it be identified as well? Whenever such user
identificatio... | computer science |
31,909 | Metric Learning across Heterogeneous Domains by Respectively Aligning
Both Priors and Posteriors | cs.LG | In this paper, we attempts to learn a single metric across two heterogeneous
domains where source domain is fully labeled and has many samples while target
domain has only a few labeled samples but abundant unlabeled samples. To the
best of our knowledge, this task is seldom touched. The proposed learning model
has a s... | computer science |
31,910 | Margin Distribution Controlled Boosting | cs.LG | Schapire's margin theory provides a theoretical explanation to the success of
boosting-type methods and manifests that a good margin distribution (MD) of
training samples is essential for generalization. However the statement that a
MD is good is vague, consequently, many recently developed algorithms try to
generate a... | computer science |
31,911 | Inverse Reinforcement Learning with Gaussian Process | cs.LG | We present new algorithms for inverse reinforcement learning (IRL, or inverse
optimal control) in convex optimization settings. We argue that finite-space
IRL can be posed as a convex quadratic program under a Bayesian inference
framework with the objective of maximum a posterior estimation. To deal with
problems in la... | computer science |
31,912 | Efficient Active Learning of Halfspaces: an Aggressive Approach | cs.LG | We study pool-based active learning of half-spaces. We revisit the aggressive
approach for active learning in the realizable case, and show that it can be
made efficient and practical, while also having theoretical guarantees under
reasonable assumptions. We further show, both theoretically and experimentally,
that it ... | computer science |
31,913 | Auto-WEKA: Combined Selection and Hyperparameter Optimization of
Classification Algorithms | cs.LG | Many different machine learning algorithms exist; taking into account each
algorithm's hyperparameters, there is a staggeringly large number of possible
alternatives overall. We consider the problem of simultaneously selecting a
learning algorithm and setting its hyperparameters, going beyond previous work
that address... | computer science |
31,914 | Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector
Fields | cs.LG | Scientists study trajectory data to understand trends in movement patterns,
such as human mobility for traffic analysis and urban planning. There is a
pressing need for scalable and efficient techniques for analyzing this data and
discovering the underlying patterns. In this paper, we introduce a novel
technique which ... | computer science |
31,915 | Link Prediction via Generalized Coupled Tensor Factorisation | cs.LG | This study deals with the missing link prediction problem: the problem of
predicting the existence of missing connections between entities of interest.
We address link prediction using coupled analysis of relational datasets
represented as heterogeneous data, i.e., datasets in the form of matrices and
higher-order tens... | computer science |
31,916 | Improving the K-means algorithm using improved downhill simplex search | cs.LG | The k-means algorithm is one of the well-known and most popular clustering
algorithms. K-means seeks an optimal partition of the data by minimizing the
sum of squared error with an iterative optimization procedure, which belongs to
the category of hill climbing algorithms. As we know hill climbing searches are
famous f... | computer science |
31,917 | Structuring Relevant Feature Sets with Multiple Model Learning | cs.LG | Feature selection is one of the most prominent learning tasks, especially in
high-dimensional datasets in which the goal is to understand the mechanisms
that underly the learning dataset. However most of them typically deliver just
a flat set of relevant features and provide no further information on what kind
of struc... | computer science |
31,918 | An Empirical Study of MAUC in Multi-class Problems with Uncertain Cost
Matrices | cs.LG | Cost-sensitive learning relies on the availability of a known and fixed cost
matrix. However, in some scenarios, the cost matrix is uncertain during
training, and re-train a classifier after the cost matrix is specified would
not be an option. For binary classification, this issue can be successfully
addressed by metho... | computer science |
31,919 | Performance Evaluation of Predictive Classifiers For Knowledge Discovery
From Engineering Materials Data Sets | cs.LG | In this paper, naive Bayesian and C4.5 Decision Tree Classifiers(DTC) are
successively applied on materials informatics to classify the engineering
materials into different classes for the selection of materials that suit the
input design specifications. Here, the classifiers are analyzed individually
and their perform... | computer science |
31,920 | Conditional validity of inductive conformal predictors | cs.LG | Conformal predictors are set predictors that are automatically valid in the
sense of having coverage probability equal to or exceeding a given confidence
level. Inductive conformal predictors are a computationally efficient version
of conformal predictors satisfying the same property of validity. However,
inductive con... | computer science |
31,921 | Improving Energy Efficiency in Femtocell Networks: A Hierarchical
Reinforcement Learning Framework | cs.LG | This paper investigates energy efficiency for two-tier femtocell networks
through combining game theory and stochastic learning. With the Stackelberg
game formulation, a hierarchical reinforcement learning framework is applied to
study the joint average utility maximization of macrocells and femtocells
subject to the m... | computer science |
31,922 | Parametric Local Metric Learning for Nearest Neighbor Classification | cs.LG | We study the problem of learning local metrics for nearest neighbor
classification. Most previous works on local metric learning learn a number of
local unrelated metrics. While this "independence" approach delivers an
increased flexibility its downside is the considerable risk of overfitting. We
present a new parametr... | computer science |
31,923 | Fast Randomized Model Generation for Shapelet-Based Time Series
Classification | cs.LG | Time series classification is a field which has drawn much attention over the
past decade. A new approach for classification of time series uses
classification trees based on shapelets. A shapelet is a subsequence extracted
from one of the time series in the dataset. A disadvantage of this approach is
the time required... | computer science |
31,924 | Towards Large-scale and Ultrahigh Dimensional Feature Selection via
Feature Generation | cs.LG | In many real-world applications such as text mining, it is desirable to
select the most relevant features or variables to improve the generalization
ability, or to provide a better interpretation of the prediction models. {In
this paper, a novel adaptive feature scaling (AFS) scheme is proposed by
introducing a feature... | computer science |
31,925 | BPRS: Belief Propagation Based Iterative Recommender System | cs.LG | In this paper we introduce the first application of the Belief Propagation
(BP) algorithm in the design of recommender systems. We formulate the
recommendation problem as an inference problem and aim to compute the marginal
probability distributions of the variables which represent the ratings to be
predicted. However,... | computer science |
31,926 | More Is Better: Large Scale Partially-supervised Sentiment
Classification - Appendix | cs.LG | We describe a bootstrapping algorithm to learn from partially labeled data,
and the results of an empirical study for using it to improve performance of
sentiment classification using up to 15 million unlabeled Amazon product
reviews. Our experiments cover semi-supervised learning, domain adaptation and
weakly supervis... | computer science |
31,927 | A Deterministic Analysis of an Online Convex Mixture of Expert
Algorithms | cs.LG | We analyze an online learning algorithm that adaptively combines outputs of
two constituent algorithms (or the experts) running in parallel to model an
unknown desired signal. This online learning algorithm is shown to achieve (and
in some cases outperform) the mean-square error (MSE) performance of the best
constituen... | computer science |
31,928 | A Novel Design Specification Distance(DSD) Based K-Mean Clustering
Performace Evluation on Engineering Materials Database | cs.LG | Organizing data into semantically more meaningful is one of the fundamental
modes of understanding and learning. Cluster analysis is a formal study of
methods for understanding and algorithm for learning. K-mean clustering
algorithm is one of the most fundamental and simple clustering algorithms. When
there is no prior... | computer science |
31,929 | Risk-Aversion in Multi-armed Bandits | cs.LG | Stochastic multi-armed bandits solve the Exploration-Exploitation dilemma and
ultimately maximize the expected reward. Nonetheless, in many practical
problems, maximizing the expected reward is not the most desirable objective.
In this paper, we introduce a novel setting based on the principle of
risk-aversion where th... | computer science |
31,930 | Error Correction in Learning using SVMs | cs.LG | This paper is concerned with learning binary classifiers under adversarial
label-noise. We introduce the problem of error-correction in learning where the
goal is to recover the original clean data from a label-manipulated version of
it, given (i) no constraints on the adversary other than an upper-bound on the
number ... | computer science |
31,931 | Learning to Optimize Via Posterior Sampling | cs.LG | This paper considers the use of a simple posterior sampling algorithm to
balance between exploration and exploitation when learning to optimize actions
such as in multi-armed bandit problems. The algorithm, also known as Thompson
Sampling, offers significant advantages over the popular upper confidence bound
(UCB) appr... | computer science |
31,932 | Efficient Learning of Domain-invariant Image Representations | cs.LG | We present an algorithm that learns representations which explicitly
compensate for domain mismatch and which can be efficiently realized as linear
classifiers. Specifically, we form a linear transformation that maps features
from the target (test) domain to the source (training) domain as part of
training the classifi... | computer science |
31,933 | Feature grouping from spatially constrained multiplicative interaction | cs.LG | We present a feature learning model that learns to encode relationships
between images. The model is defined as a Gated Boltzmann Machine, which is
constrained such that hidden units that are nearby in space can gate each
other's connections. We show how frequency/orientation "columns" as well as
topographic filter map... | computer science |
31,934 | A Semantic Matching Energy Function for Learning with Multi-relational
Data | cs.LG | Large-scale relational learning becomes crucial for handling the huge amounts
of structured data generated daily in many application domains ranging from
computational biology or information retrieval, to natural language processing.
In this paper, we present a new neural network architecture designed to embed
multi-re... | computer science |
31,935 | How good is the Electricity benchmark for evaluating concept drift
adaptation | cs.LG | In this correspondence, we will point out a problem with testing adaptive
classifiers on autocorrelated data. In such a case random change alarms may
boost the accuracy figures. Hence, we cannot be sure if the adaptation is
working well. | computer science |
31,936 | Learning Features with Structure-Adapting Multi-view Exponential Family
Harmoniums | cs.LG | We proposea graphical model for multi-view feature extraction that
automatically adapts its structure to achieve better representation of data
distribution. The proposed model, structure-adapting multi-view harmonium
(SA-MVH) has switch parameters that control the connection between hidden nodes
and input views, and le... | computer science |
31,937 | Saturating Auto-Encoders | cs.LG | We introduce a simple new regularizer for auto-encoders whose hidden-unit
activation functions contain at least one zero-gradient (saturated) region.
This regularizer explicitly encourages activations in the saturated region(s)
of the corresponding activation function. We call these Saturating
Auto-Encoders (SATAE). We... | computer science |
31,938 | Behavior Pattern Recognition using A New Representation Model | cs.LG | We study the use of inverse reinforcement learning (IRL) as a tool for the
recognition of agents' behavior on the basis of observation of their sequential
decision behavior interacting with the environment. We model the problem faced
by the agents as a Markov decision process (MDP) and model the observed
behavior of th... | computer science |
31,939 | Switched linear encoding with rectified linear autoencoders | cs.LG | Several recent results in machine learning have established formal
connections between autoencoders---artificial neural network models that
attempt to reproduce their inputs---and other coding models like sparse coding
and K-means. This paper explores in depth an autoencoder model that is
constructed using rectified li... | computer science |
31,940 | Learning Output Kernels for Multi-Task Problems | cs.LG | Simultaneously solving multiple related learning tasks is beneficial under a
variety of circumstances, but the prior knowledge necessary to correctly model
task relationships is rarely available in practice. In this paper, we develop a
novel kernel-based multi-task learning technique that automatically reveals
structur... | computer science |
31,941 | See the Tree Through the Lines: The Shazoo Algorithm -- Full Version -- | cs.LG | Predicting the nodes of a given graph is a fascinating theoretical problem
with applications in several domains. Since graph sparsification via spanning
trees retains enough information while making the task much easier, trees are
an important special case of this problem. Although it is known how to predict
the nodes ... | computer science |
31,942 | Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic
Regret | cs.LG | In online learning the performance of an algorithm is typically compared to
the performance of a fixed function from some class, with a quantity called
regret. Forster proposed a last-step min-max algorithm which was somewhat
simpler than the algorithm of Vovk, yet with the same regret. In fact the
algorithm he analyze... | computer science |
31,943 | Hierarchical Data Representation Model - Multi-layer NMF | cs.LG | In this paper, we propose a data representation model that demonstrates
hierarchical feature learning using nsNMF. We extend unit algorithm into
several layers. Experiments with document and image data successfully
discovered feature hierarchies. We also prove that proposed method results in
much better classification ... | computer science |
31,944 | Clustering-Based Matrix Factorization | cs.LG | Recommender systems are emerging technologies that nowadays can be found in
many applications such as Amazon, Netflix, and so on. These systems help users
to find relevant information, recommendations, and their preferred items.
Slightly improvement of the accuracy of these recommenders can highly affect
the quality of... | computer science |
31,945 | O(logT) Projections for Stochastic Optimization of Smooth and Strongly
Convex Functions | cs.LG | Traditional algorithms for stochastic optimization require projecting the
solution at each iteration into a given domain to ensure its feasibility. When
facing complex domains, such as positive semi-definite cones, the projection
operation can be expensive, leading to a high computational cost per iteration.
In this pa... | computer science |
31,946 | Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch
Stochastic Gradient Descent (SGD) | cs.LG | Distance metric learning (DML) is an important task that has found
applications in many domains. The high computational cost of DML arises from
the large number of variables to be determined and the constraint that a
distance metric has to be a positive semi-definite (PSD) matrix. Although
stochastic gradient descent (... | computer science |
31,947 | Fast SVM training using approximate extreme points | cs.LG | Applications of non-linear kernel Support Vector Machines (SVMs) to large
datasets is seriously hampered by its excessive training time. We propose a
modification, called the approximate extreme points support vector machine
(AESVM), that is aimed at overcoming this burden. Our approach relies on
conducting the SVM opt... | computer science |
31,948 | A Generalized Online Mirror Descent with Applications to Classification
and Regression | cs.LG | Online learning algorithms are fast, memory-efficient, easy to implement, and
applicable to many prediction problems, including classification, regression,
and ranking. Several online algorithms were proposed in the past few decades,
some based on additive updates, like the Perceptron, and some on multiplicative
update... | computer science |
31,949 | A New Homogeneity Inter-Clusters Measure in SemiSupervised Clustering | cs.LG | Many studies in data mining have proposed a new learning called
semi-Supervised. Such type of learning combines unlabeled and labeled data
which are hard to obtain. However, in unsupervised methods, the only unlabeled
data are used. The problem of significance and the effectiveness of
semi-supervised clustering results... | computer science |
31,950 | A Survey on Multi-view Learning | cs.LG | In recent years, a great many methods of learning from multi-view data by
considering the diversity of different views have been proposed. These views
may be obtained from multiple sources or different feature subsets. In trying
to organize and highlight similarities and differences between the variety of
multi-view le... | computer science |
31,951 | Continuum armed bandit problem of few variables in high dimensions | cs.LG | We consider the stochastic and adversarial settings of continuum armed
bandits where the arms are indexed by [0,1]^d. The reward functions r:[0,1]^d
-> R are assumed to intrinsically depend on at most k coordinate variables
implying r(x_1,..,x_d) = g(x_{i_1},..,x_{i_k}) for distinct and unknown
i_1,..,i_k from {1,..,d}... | computer science |
31,952 | Irreflexive and Hierarchical Relations as Translations | cs.LG | We consider the problem of embedding entities and relations of knowledge
bases in low-dimensional vector spaces. Unlike most existing approaches, which
are primarily efficient for modeling equivalence relations, our approach is
designed to explicitly model irreflexive relations, such as hierarchies, by
interpreting the... | computer science |
31,953 | Fractal structures in Adversarial Prediction | cs.LG | Fractals are self-similar recursive structures that have been used in
modeling several real world processes. In this work we study how "fractal-like"
processes arise in a prediction game where an adversary is generating a
sequence of bits and an algorithm is trying to predict them. We will see that
under a certain form... | computer science |
31,954 | Understanding ACT-R - an Outsider's Perspective | cs.LG | The ACT-R theory of cognition developed by John Anderson and colleagues
endeavors to explain how humans recall chunks of information and how they solve
problems. ACT-R also serves as a theoretical basis for "cognitive tutors",
i.e., automatic tutoring systems that help students learn mathematics, computer
programming, ... | computer science |
31,955 | Guided Random Forest in the RRF Package | cs.LG | Random Forest (RF) is a powerful supervised learner and has been popularly
used in many applications such as bioinformatics.
In this work we propose the guided random forest (GRF) for feature selection.
Similar to a feature selection method called guided regularized random forest
(GRRF), GRF is built using the import... | computer science |
31,956 | Deep Generative Stochastic Networks Trainable by Backprop | cs.LG | We introduce a novel training principle for probabilistic models that is an
alternative to maximum likelihood. The proposed Generative Stochastic Networks
(GSN) framework is based on learning the transition operator of a Markov chain
whose stationary distribution estimates the data distribution. The transition
distribu... | computer science |
31,957 | Performance analysis of unsupervised feature selection methods | cs.LG | Feature selection (FS) is a process which attempts to select more informative
features. In some cases, too many redundant or irrelevant features may
overpower main features for classification. Feature selection can remedy this
problem and therefore improve the prediction accuracy and reduce the
computational overhead o... | computer science |
31,958 | Auditing: Active Learning with Outcome-Dependent Query Costs | cs.LG | We propose a learning setting in which unlabeled data is free, and the cost
of a label depends on its value, which is not known in advance. We study binary
classification in an extreme case, where the algorithm only pays for negative
labels. Our motivation are applications such as fraud detection, in which
investigatin... | computer science |
31,959 | Guaranteed Classification via Regularized Similarity Learning | cs.LG | Learning an appropriate (dis)similarity function from the available data is a
central problem in machine learning, since the success of many machine learning
algorithms critically depends on the choice of a similarity function to compare
examples. Despite many approaches for similarity metric learning have been
propose... | computer science |
31,960 | On-line PCA with Optimal Regrets | cs.LG | We carefully investigate the on-line version of PCA, where in each trial a
learning algorithm plays a k-dimensional subspace, and suffers the compression
loss on the next instance when projected into the chosen subspace. In this
setting, we analyze two popular on-line algorithms, Gradient Descent (GD) and
Exponentiated... | computer science |
31,961 | Multiarmed Bandits With Limited Expert Advice | cs.LG | We solve the COLT 2013 open problem of \citet{SCB} on minimizing regret in
the setting of advice-efficient multiarmed bandits with expert advice. We give
an algorithm for the setting of K arms and N experts out of which we are
allowed to query and use only M experts' advices in each round, which has a
regret bound of \... | computer science |
31,962 | Machine Teaching for Bayesian Learners in the Exponential Family | cs.LG | What if there is a teacher who knows the learning goal and wants to design
good training data for a machine learner? We propose an optimal teaching
framework aimed at learners who employ Bayesian models. Our framework is
expressed as an optimization problem over teaching examples that balance the
future loss of the lea... | computer science |
31,963 | Song-based Classification techniques for Endangered Bird Conservation | cs.LG | The work presented in this paper is part of a global framework which long
term goal is to design a wireless sensor network able to support the
observation of a population of endangered birds. We present the first stage for
which we have conducted a knowledge discovery approach on a sample of
acoustical data. We use MFC... | computer science |
31,964 | Model Reframing by Feature Context Change | cs.LG | The feature space (including both input and output variables) characterises a
data mining problem. In predictive (supervised) problems, the quality and
availability of features determines the predictability of the dependent
variable, and the performance of data mining models in terms of
misclassification or regression ... | computer science |
31,965 | Prediction with expert advice for the Brier game | cs.LG | We show that the Brier game of prediction is mixable and find the optimal
learning rate and substitution function for it. The resulting prediction
algorithm is applied to predict results of football and tennis matches. The
theoretical performance guarantee turns out to be rather tight on these data
sets, especially in ... | computer science |
31,966 | Consistency of trace norm minimization | cs.LG | Regularization by the sum of singular values, also referred to as the trace
norm, is a popular technique for estimating low rank rectangular matrices. In
this paper, we extend some of the consistency results of the Lasso to provide
necessary and sufficient conditions for rank consistency of trace norm
minimization with... | computer science |
31,967 | Learning Isometric Separation Maps | cs.LG | Maximum Variance Unfolding (MVU) and its variants have been very successful
in embedding data-manifolds in lower dimensional spaces, often revealing the
true intrinsic dimension. In this paper we show how to also incorporate
supervised class information into an MVU-like method without breaking its
convexity. We call th... | computer science |
31,968 | Randomized Algorithms for Large scale SVMs | cs.LG | We propose a randomized algorithm for training Support vector machines(SVMs)
on large datasets. By using ideas from Random projections we show that the
combinatorial dimension of SVMs is $O({log} n)$ with high probability. This
estimate of combinatorial dimension is used to derive an iterative algorithm,
called RandSVM... | computer science |
31,969 | Scalable Inference for Latent Dirichlet Allocation | cs.LG | We investigate the problem of learning a topic model - the well-known Latent
Dirichlet Allocation - in a distributed manner, using a cluster of C processors
and dividing the corpus to be learned equally among them. We propose a simple
approximated method that can be tuned, trading speed for accuracy according to
the ta... | computer science |
31,970 | GraphLab: A Distributed Framework for Machine Learning in the Cloud | cs.LG | Machine Learning (ML) techniques are indispensable in a wide range of fields.
Unfortunately, the exponential increase of dataset sizes are rapidly extending
the runtime of sequential algorithms and threatening to slow future progress in
ML. With the promise of affordable large-scale parallel computing, Cloud
systems of... | computer science |
31,971 | Towards Optimal One Pass Large Scale Learning with Averaged Stochastic
Gradient Descent | cs.LG | For large scale learning problems, it is desirable if we can obtain the
optimal model parameters by going through the data in only one pass. Polyak and
Juditsky (1992) showed that asymptotically the test performance of the simple
average of the parameters obtained by stochastic gradient descent (SGD) is as
good as that... | computer science |
31,972 | Discovering Knowledge using a Constraint-based Language | cs.LG | Discovering pattern sets or global patterns is an attractive issue from the
pattern mining community in order to provide useful information. By combining
local patterns satisfying a joint meaning, this approach produces patterns of
higher level and thus more useful for the data analyst than the usual local
patterns, wh... | computer science |
31,973 | On the Universality of Online Mirror Descent | cs.LG | We show that for a general class of convex online learning problems, Mirror
Descent can always achieve a (nearly) optimal regret guarantee. | computer science |
31,974 | The Divergence of Reinforcement Learning Algorithms with Value-Iteration
and Function Approximation | cs.LG | This paper gives specific divergence examples of value-iteration for several
major Reinforcement Learning and Adaptive Dynamic Programming algorithms, when
using a function approximator for the value function. These divergence examples
differ from previous divergence examples in the literature, in that they are
applica... | computer science |
31,975 | Axioms for Rational Reinforcement Learning | cs.LG | We provide a formal, simple and intuitive theory of rational decision making
including sequential decisions that affect the environment. The theory has a
geometric flavor, which makes the arguments easy to visualize and understand.
Our theory is for complete decision makers, which means that they have a
complete set of... | computer science |
31,976 | Automatic Network Reconstruction using ASP | cs.LG | Building biological models by inferring functional dependencies from
experimental data is an im- portant issue in Molecular Biology. To relieve the
biologist from this traditionally manual process, various approaches have been
proposed to increase the degree of automation. However, available ap- proaches
often yield a ... | computer science |
31,977 | Multiclass learnability and the ERM principle | cs.LG | We study the sample complexity of multiclass prediction in several learning
settings. For the PAC setting our analysis reveals a surprising phenomenon: In
sharp contrast to binary classification, we show that there exist multiclass
hypothesis classes for which some Empirical Risk Minimizers (ERM learners) have
lower sa... | computer science |
31,978 | Estimating or Propagating Gradients Through Stochastic Neurons for
Conditional Computation | cs.LG | Stochastic neurons and hard non-linearities 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 or non-smooth neurons? I.e., can we "back-propagate"
through these st... | computer science |
31,979 | Stochastic Optimization for Machine Learning | cs.LG | It has been found that stochastic algorithms often find good solutions much
more rapidly than inherently-batch approaches. Indeed, a very useful rule of
thumb is that often, when solving a machine learning problem, an iterative
technique which relies on performing a very large number of
relatively-inexpensive updates w... | computer science |
31,980 | Knapsack Constrained Contextual Submodular List Prediction with
Application to Multi-document Summarization | cs.LG | We study the problem of predicting a set or list of options under knapsack
constraint. The quality of such lists are evaluated by a submodular reward
function that measures both quality and diversity. Similar to DAgger (Ross et
al., 2010), by a reduction to online learning, we show how to adapt two
sequence prediction ... | computer science |
31,981 | Comment on "robustness and regularization of support vector machines" by
H. Xu, et al., (Journal of Machine Learning Research, vol. 10, pp. 1485-1510,
2009, arXiv:0803.3490) | cs.LG | This paper comments on the published work dealing with robustness and
regularization of support vector machines (Journal of Machine Learning
Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc. They
proposed a theorem to show that it is possible to relate robustness in the
feature space and robustne... | computer science |
31,982 | The Sample-Complexity of General Reinforcement Learning | cs.LG | We present a new algorithm for general reinforcement learning where the true
environment is known to belong to a finite class of N arbitrary models. The
algorithm is shown to be near-optimal for all but O(N log^2 N) time-steps with
high probability. Infinite classes are also considered where we show that
compactness is... | computer science |
31,983 | Ensemble of Distributed Learners for Online Classification of Dynamic
Data Streams | cs.LG | We present an efficient distributed online learning scheme to classify data
captured from distributed, heterogeneous, and dynamic data sources. Our scheme
consists of multiple distributed local learners, that analyze different streams
of data that are correlated to a common event that needs to be classified. Each
learn... | computer science |
31,984 | Prediction of breast cancer recurrence using Classification Restricted
Boltzmann Machine with Dropping | cs.LG | In this paper, we apply Classification Restricted Boltzmann Machine
(ClassRBM) to the problem of predicting breast cancer recurrence. According to
the Polish National Cancer Registry, in 2010 only, the breast cancer caused
almost 25% of all diagnosed cases of cancer in Poland. We propose how to use
ClassRBM for predict... | computer science |
31,985 | A Novel Clustering Algorithm Based on Quantum Random Walk | cs.LG | The enormous successes have been made by quantum algorithms during the last
decade. In this paper, we combine the quantum random walk (QRW) with the
problem of data clustering, and develop two clustering algorithms based on the
one dimensional QRW. Then, the probability distributions on the positions
induced by QRW in ... | computer science |
31,986 | Convex Sparse Matrix Factorizations | cs.LG | We present a convex formulation of dictionary learning for sparse signal
decomposition. Convexity is obtained by replacing the usual explicit upper
bound on the dictionary size by a convex rank-reducing term similar to the
trace norm. In particular, our formulation introduces an explicit trade-off
between size and spar... | computer science |
31,987 | Binary Classification Based on Potentials | cs.LG | We introduce a simple and computationally trivial method for binary
classification based on the evaluation of potential functions. We demonstrate
that despite the conceptual and computational simplicity of the method its
performance can match or exceed that of standard Support Vector Machine
methods. | computer science |
31,988 | Linearly Parameterized Bandits | cs.LG | We consider bandit problems involving a large (possibly infinite) collection
of arms, in which the expected reward of each arm is a linear function of an
$r$-dimensional random vector $\mathbf{Z} \in \mathbb{R}^r$, where $r \geq 2$.
The objective is to minimize the cumulative regret and Bayes risk. When the set
of arms... | computer science |
31,989 | Importance Weighted Active Learning | cs.LG | We present a practical and statistically consistent scheme for actively
learning binary classifiers under general loss functions. Our algorithm uses
importance weighting to correct sampling bias, and by controlling the variance,
we are able to give rigorous label complexity bounds for the learning process.
Experiments ... | computer science |
31,990 | Efficient Human Computation | cs.LG | Collecting large labeled data sets is a laborious and expensive task, whose
scaling up requires division of the labeling workload between many teachers.
When the number of classes is large, miscorrespondences between the labels
given by the different teachers are likely to occur, which, in the extreme
case, may reach t... | computer science |
31,991 | Differential Contrastive Divergence | cs.LG | This paper has been retracted. | computer science |
31,992 | On $p$-adic Classification | cs.LG | A $p$-adic modification of the split-LBG classification method is presented
in which first clusterings and then cluster centers are computed which locally
minimise an energy function. The outcome for a fixed dataset is independent of
the prime number $p$ with finitely many exceptions. The methods are applied to
the con... | computer science |
31,993 | Equations of States in Statistical Learning for a Nonparametrizable and
Regular Case | cs.LG | Many learning machines that have hierarchical structure or hidden variables
are now being used in information science, artificial intelligence, and
bioinformatics. However, several learning machines used in such fields are not
regular but singular statistical models, hence their generalization performance
is still left... | computer science |
31,994 | An optimal linear separator for the Sonar Signals Classification task | cs.LG | The problem of classifying sonar signals from rocks and mines first studied
by Gorman and Sejnowski has become a benchmark against which many learning
algorithms have been tested. We show that both the training set and the test
set of this benchmark are linearly separable, although with different
hyperplanes. Moreover,... | computer science |
31,995 | Bayesian History Reconstruction of Complex Human Gene Clusters on a
Phylogeny | cs.LG | Clusters of genes that have evolved by repeated segmental duplication present
difficult challenges throughout genomic analysis, from sequence assembly to
functional analysis. Improved understanding of these clusters is of utmost
importance, since they have been shown to be the source of evolutionary
innovation, and hav... | computer science |
31,996 | Bayesian two-sample tests | cs.LG | In this paper, we present two classes of Bayesian approaches to the
two-sample problem. Our first class of methods extends the Bayesian t-test to
include all parametric models in the exponential family and their conjugate
priors. Our second class of methods uses Dirichlet process mixtures (DPM) of
such conjugate-expone... | computer science |
31,997 | Acquiring Knowledge for Evaluation of Teachers Performance in Higher
Education using a Questionnaire | cs.LG | In this paper, we present the step by step knowledge acquisition process by
choosing a structured method through using a questionnaire as a knowledge
acquisition tool. Here we want to depict the problem domain as, how to evaluate
teachers performance in higher education through the use of expert system
technology. The ... | computer science |
31,998 | Unsupervised Search-based Structured Prediction | cs.LG | We describe an adaptation and application of a search-based structured
prediction algorithm "Searn" to unsupervised learning problems. We show that it
is possible to reduce unsupervised learning to supervised learning and
demonstrate a high-quality unsupervised shift-reduce parsing model. We
additionally show a close c... | computer science |
31,999 | Random DFAs are Efficiently PAC Learnable | cs.LG | This paper has been withdrawn due to an error found by Dana Angluin and Lev
Reyzin. | computer science |
32,000 | Bayesian Multitask Learning with Latent Hierarchies | cs.LG | We learn multiple hypotheses for related tasks under a latent hierarchical
relationship between tasks. We exploit the intuition that for domain
adaptation, we wish to share classifier structure, but for multitask learning,
we wish to share covariance structure. Our hierarchical model is seen to
subsume several previous... | computer science |
32,001 | A Bayesian Model for Supervised Clustering with the Dirichlet Process
Prior | cs.LG | We develop a Bayesian framework for tackling the supervised clustering
problem, the generic problem encountered in tasks such as reference matching,
coreference resolution, identity uncertainty and record linkage. Our clustering
model is based on the Dirichlet process prior, which enables us to define
distributions ove... | computer science |
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