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32,102 | A Last-Step Regression Algorithm for Non-Stationary Online Learning | cs.LG | The goal of a learner in standard online learning is to maintain an average
loss close to the loss of the best-performing single function in some class. In
many real-world problems, such as rating or ranking items, there is no single
best target function during the runtime of the algorithm, instead the best
(local) tar... | computer science |
32,103 | A Quorum Sensing Inspired Algorithm for Dynamic Clustering | cs.LG | Quorum sensing is a decentralized biological process, through which a
community of cells with no global awareness coordinate their functional
behaviors based solely on cell-medium interactions and local decisions. This
paper draws inspirations from quorum sensing and colony competition to derive a
new algorithm for dat... | computer science |
32,104 | On multi-class learning through the minimization of the confusion matrix
norm | cs.LG | In imbalanced multi-class classification problems, the misclassification rate
as an error measure may not be a relevant choice. Several methods have been
developed where the performance measure retained richer information than the
mere misclassification rate: misclassification costs, ROC-based information,
etc. Followi... | computer science |
32,105 | Markov Chain Monte Carlo for Arrangement of Hyperplanes in
Locality-Sensitive Hashing | cs.LG | Since Hamming distances can be calculated by bitwise computations, they can
be calculated with less computational load than L2 distances. Similarity
searches can therefore be performed faster in Hamming distance space. The
elements of Hamming distance space are bit strings. On the other hand, the
arrangement of hyperpl... | computer science |
32,106 | Large-Scale Learning with Less RAM via Randomization | cs.LG | We reduce the memory footprint of popular large-scale online learning methods
by projecting our weight vector onto a coarse discrete set using randomized
rounding. Compared to standard 32-bit float encodings, this reduces RAM usage
by more than 50% during training and by up to 95% when making predictions from
a fixed m... | computer science |
32,107 | A Note on k-support Norm Regularized Risk Minimization | cs.LG | The k-support norm has been recently introduced to perform correlated
sparsity regularization. Although Argyriou et al. only reported experiments
using squared loss, here we apply it to several other commonly used settings
resulting in novel machine learning algorithms with interesting and familiar
limit cases. Source ... | computer science |
32,108 | Inductive Hashing on Manifolds | cs.LG | Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes that
preserve the Euclidean distance in the original space. Manifold learning
techniques, in contr... | computer science |
32,109 | Relative Comparison Kernel Learning with Auxiliary Kernels | cs.LG | In this work we consider the problem of learning a positive semidefinite
kernel matrix from relative comparisons of the form: "object A is more similar
to object B than it is to C", where comparisons are given by humans. Existing
solutions to this problem assume many comparisons are provided to learn a high
quality ker... | computer science |
32,110 | Group Learning and Opinion Diffusion in a Broadcast Network | cs.LG | We analyze the following group learning problem in the context of opinion
diffusion: Consider a network with $M$ users, each facing $N$ options. In a
discrete time setting, at each time step, each user chooses $K$ out of the $N$
options, and receive randomly generated rewards, whose statistics depend on the
options cho... | computer science |
32,111 | A Metric-learning based framework for Support Vector Machines and
Multiple Kernel Learning | cs.LG | Most metric learning algorithms, as well as Fisher's Discriminant Analysis
(FDA), optimize some cost function of different measures of within-and
between-class distances. On the other hand, Support Vector Machines(SVMs) and
several Multiple Kernel Learning (MKL) algorithms are based on the SVM large
margin theory. Rece... | computer science |
32,112 | Stochastic Bound Majorization | cs.LG | Recently a majorization method for optimizing partition functions of
log-linear models was proposed alongside a novel quadratic variational
upper-bound. In the batch setting, it outperformed state-of-the-art first- and
second-order optimization methods on various learning tasks. We propose a
stochastic version of this ... | computer science |
32,113 | A Kernel Classification Framework for Metric Learning | cs.LG | Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN... | computer science |
32,114 | Fenchel Duals for Drifting Adversaries | cs.LG | We describe a primal-dual framework for the design and analysis of online
convex optimization algorithms for {\em drifting regret}. Existing literature
shows (nearly) optimal drifting regret bounds only for the $\ell_2$ and the
$\ell_1$-norms. Our work provides a connection between these algorithms and the
Online Mirro... | computer science |
32,115 | On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori
Perturbations | cs.LG | In this paper we describe how MAP inference can be used to sample efficiently
from Gibbs distributions. Specifically, we provide means for drawing either
approximate or unbiased samples from Gibbs' distributions by introducing low
dimensional perturbations and solving the corresponding MAP assignments. Our
approach als... | computer science |
32,116 | An Extensive Experimental Study on the Cluster-based Reference Set
Reduction for speeding-up the k-NN Classifier | cs.LG | The k-Nearest Neighbor (k-NN) classification algorithm is one of the most
widely-used lazy classifiers because of its simplicity and ease of
implementation. It is considered to be an effective classifier and has many
applications. However, its major drawback is that when sequential search is
used to find the neighbors,... | computer science |
32,117 | On the Feature Discovery for App Usage Prediction in Smartphones | cs.LG | With the increasing number of mobile Apps developed, they are now closely
integrated into daily life. In this paper, we develop a framework to predict
mobile Apps that are most likely to be used regarding the current device status
of a smartphone. Such an Apps usage prediction framework is a crucial
prerequisite for fa... | computer science |
32,118 | Thompson Sampling for Budgeted Multi-armed Bandits | cs.LG | Thompson sampling is one of the earliest randomized algorithms for
multi-armed bandits (MAB). In this paper, we extend the Thompson sampling to
Budgeted MAB, where there is random cost for pulling an arm and the total cost
is constrained by a budget. We start with the case of Bernoulli bandits, in
which the random rewa... | computer science |
32,119 | Theory of Optimizing Pseudolinear Performance Measures: Application to
F-measure | cs.LG | Non-linear performance measures are widely used for the evaluation of
learning algorithms. For example, $F$-measure is a commonly used performance
measure for classification problems in machine learning and information
retrieval community. We study the theoretical properties of a subset of
non-linear performance measur... | computer science |
32,120 | Can deep learning help you find the perfect match? | cs.LG | Is he/she my type or not? The answer to this question depends on the personal
preferences of the one asking it. The individual process of obtaining a full
answer may generally be difficult and time consuming, but often an approximate
answer can be obtained simply by looking at a photo of the potential match.
Such appro... | computer science |
32,121 | Reinforcement Learning Neural Turing Machines - Revised | cs.LG | The Neural Turing Machine (NTM) is more expressive than all previously
considered models because of its external memory. It can be viewed as a broader
effort to use abstract external Interfaces and to learn a parametric model that
interacts with them.
The capabilities of a model can be extended by providing it with p... | computer science |
32,122 | Reinforced Decision Trees | cs.LG | In order to speed-up classification models when facing a large number of
categories, one usual approach consists in organizing the categories in a
particular structure, this structure being then used as a way to speed-up the
prediction computation. This is for example the case when using
error-correcting codes or even ... | computer science |
32,123 | A Comprehensive Study On The Applications Of Machine Learning For
Diagnosis Of Cancer | cs.LG | Collectively, lung cancer, breast cancer and melanoma was diagnosed in over
535,340 people out of which, 209,400 deaths were reported [13]. It is estimated
that over 600,000 people will be diagnosed with these forms of cancer in 2015.
Most of the deaths from lung cancer, breast cancer and melanoma result due to
late de... | computer science |
32,124 | Learning and Optimization with Submodular Functions | cs.LG | In many naturally occurring optimization problems one needs to ensure that
the definition of the optimization problem lends itself to solutions that are
tractable to compute. In cases where exact solutions cannot be computed
tractably, it is beneficial to have strong guarantees on the tractable
approximate solutions. I... | computer science |
32,125 | A Survey of Predictive Modelling under Imbalanced Distributions | cs.LG | Many real world data mining applications involve obtaining predictive models
using data sets with strongly imbalanced distributions of the target variable.
Frequently, the least common values of this target variable are associated with
events that are highly relevant for end users (e.g. fraud detection, unusual
returns... | computer science |
32,126 | Bounded-Distortion Metric Learning | cs.LG | Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} ... | computer science |
32,127 | Safe Screening for Multi-Task Feature Learning with Multiple Data
Matrices | cs.LG | Multi-task feature learning (MTFL) is a powerful technique in boosting the
predictive performance by learning multiple related
classification/regression/clustering tasks simultaneously. However, solving the
MTFL problem remains challenging when the feature dimension is extremely large.
In this paper, we propose a novel... | computer science |
32,128 | Shrinkage degree in $L_2$-re-scale boosting for regression | cs.LG | Re-scale boosting (RBoosting) is a variant of boosting which can essentially
improve the generalization performance of boosting learning. The key feature of
RBoosting lies in introducing a shrinkage degree to re-scale the ensemble
estimate in each gradient-descent step. Thus, the shrinkage degree determines
the perform... | computer science |
32,129 | Ensemble of Example-Dependent Cost-Sensitive Decision Trees | cs.LG | Several real-world classification problems are example-dependent
cost-sensitive in nature, where the costs due to misclassification vary between
examples and not only within classes. However, standard classification methods
do not take these costs into account, and assume a constant cost of
misclassification errors. In... | computer science |
32,130 | Learning with a Drifting Target Concept | cs.LG | We study the problem of learning in the presence of a drifting target
concept. Specifically, we provide bounds on the error rate at a given time,
given a learner with access to a history of independent samples labeled
according to a target concept that can change on each round. One of our main
contributions is a refine... | computer science |
32,131 | Bounds on the Minimax Rate for Estimating a Prior over a VC Class from
Independent Learning Tasks | cs.LG | We study the optimal rates of convergence for estimating a prior distribution
over a VC class from a sequence of independent data sets respectively labeled
by independent target functions sampled from the prior. We specifically derive
upper and lower bounds on the optimal rates under a smoothness condition on the
corre... | computer science |
32,132 | Safe Policy Search for Lifelong Reinforcement Learning with Sublinear
Regret | cs.LG | Lifelong reinforcement learning provides a promising framework for developing
versatile agents that can accumulate knowledge over a lifetime of experience
and rapidly learn new tasks by building upon prior knowledge. However, current
lifelong learning methods exhibit non-vanishing regret as the amount of
experience inc... | computer science |
32,133 | Instant Learning: Parallel Deep Neural Networks and Convolutional
Bootstrapping | cs.LG | Although deep neural networks (DNN) are able to scale with direct advances in
computational power (e.g., memory and processing speed), they are not well
suited to exploit the recent trends for parallel architectures. In particular,
gradient descent is a sequential process and the resulting serial dependencies
mean that... | computer science |
32,134 | Monotonic Calibrated Interpolated Look-Up Tables | cs.LG | Real-world machine learning applications may require functions that are
fast-to-evaluate and interpretable. In particular, guaranteed monotonicity of
the learned function can be critical to user trust. We propose meeting these
goals for low-dimensional machine learning problems by learning flexible,
monotonic functions... | computer science |
32,135 | Domain Adaptation Extreme Learning Machines for Drift Compensation in
E-nose Systems | cs.LG | This paper addresses an important issue, known as sensor drift that behaves a
nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of
machine learning. Traditional methods for drift compensation are laborious and
costly due to the frequent acquisition and labeling process for gases samples
recalib... | computer science |
32,136 | Efficient Elastic Net Regularization for Sparse Linear Models | cs.LG | This paper presents an algorithm for efficient training of sparse linear
models with elastic net regularization. Extending previous work on delayed
updates, the new algorithm applies stochastic gradient updates to non-zero
features only, bringing weights current as needed with closed-form updates.
Closed-form delayed u... | computer science |
32,137 | Differentially Private Distributed Online Learning | cs.LG | Online learning has been in the spotlight from the machine learning society
for a long time. To handle massive data in Big Data era, one single learner
could never efficiently finish this heavy task. Hence, in this paper, we
propose a novel distributed online learning algorithm to solve the problem.
Comparing to typica... | computer science |
32,138 | Fantasy Football Prediction | cs.LG | The ubiquity of professional sports and specifically the NFL have lead to an
increase in popularity for Fantasy Football. Users have many tools at their
disposal: statistics, predictions, rankings of experts and even recommendations
of peers. There are issues with all of these, though. Especially since many
people pay ... | computer science |
32,139 | Learning with Symmetric Label Noise: The Importance of Being Unhinged | cs.LG | Convex potential minimisation is the de facto approach to binary
classification. However, Long and Servedio [2010] proved that under symmetric
label noise (SLN), minimisation of any convex potential over a linear function
class can result in classification performance equivalent to random guessing.
This ostensibly show... | computer science |
32,140 | Topic Model Based Multi-Label Classification from the Crowd | cs.LG | Multi-label classification is a common supervised machine learning problem
where each instance is associated with multiple classes. The key challenge in
this problem is learning the correlations between the classes. An additional
challenge arises when the labels of the training instances are provided by
noisy, heteroge... | computer science |
32,141 | Towards Label Imbalance in Multi-label Classification with Many Labels | cs.LG | In multi-label classification, an instance may be associated with a set of
labels simultaneously. Recently, the research on multi-label classification has
largely shifted its focus to the other end of the spectrum where the number of
labels is assumed to be extremely large. The existing works focus on how to
design sca... | computer science |
32,142 | Self-Paced Multi-Task Learning | cs.LG | In this paper, we propose a novel multi-task learning (MTL) framework, called
Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating
all tasks and instances equally when training, SPMTL attempts to jointly learn
the tasks by taking into consideration the complexities of both tasks and
instances.... | computer science |
32,143 | Simple and Efficient Learning using Privileged Information | cs.LG | The Support Vector Machine using Privileged Information (SVM+) has been
proposed to train a classifier to utilize the additional privileged information
that is only available in the training phase but not available in the test
phase. In this work, we propose an efficient solution for SVM+ by simply
utilizing the square... | computer science |
32,144 | Relationship between Variants of One-Class Nearest Neighbours and
Creating their Accurate Ensembles | cs.LG | In one-class classification problems, only the data for the target class is
available, whereas the data for the non-target class may be completely absent.
In this paper, we study one-class nearest neighbour (OCNN) classifiers and
their different variants. We present a theoretical analysis to show the
relationships amon... | computer science |
32,145 | Generalising the Discriminative Restricted Boltzmann Machine | cs.LG | We present a novel theoretical result that generalises the Discriminative
Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined
assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this
result makes it possible to derive cost functions for variants of the DRBM that
utilise other ... | computer science |
32,146 | Efficient Globally Convergent Stochastic Optimization for Canonical
Correlation Analysis | cs.LG | We study the stochastic optimization of canonical correlation analysis (CCA),
whose objective is nonconvex and does not decouple over training samples.
Although several stochastic gradient based optimization algorithms have been
recently proposed to solve this problem, no global convergence guarantee was
provided by an... | computer science |
32,147 | Probabilistic classifiers with low rank indefinite kernels | cs.LG | Indefinite similarity measures can be frequently found in bio-informatics by
means of alignment scores, but are also common in other fields like shape
measures in image retrieval. Lacking an underlying vector space, the data are
given as pairwise similarities only. The few algorithms available for such data
do not scal... | computer science |
32,148 | Online Learning of Portfolio Ensembles with Sector Exposure
Regularization | cs.LG | We consider online learning of ensembles of portfolio selection algorithms
and aim to regularize risk by encouraging diversification with respect to a
predefined risk-driven grouping of stocks. Our procedure uses online convex
optimization to control capital allocation to underlying investment algorithms
while encourag... | computer science |
32,149 | Optimal Margin Distribution Machine | cs.LG | Support vector machine (SVM) has been one of the most popular learning
algorithms, with the central idea of maximizing the minimum margin, i.e., the
smallest distance from the instances to the classification boundary. Recent
theoretical results, however, disclosed that maximizing the minimum margin does
not necessarily... | computer science |
32,150 | Animation and Chirplet-Based Development of a PIR Sensor Array for
Intruder Classification in an Outdoor Environment | cs.LG | This paper presents the development of a passive infra-red sensor tower
platform along with a classification algorithm to distinguish between human
intrusion, animal intrusion and clutter arising from wind-blown vegetative
movement in an outdoor environment. The research was aimed at exploring the
potential use of wire... | computer science |
32,151 | Max-Information, Differential Privacy, and Post-Selection Hypothesis
Testing | cs.LG | In this paper, we initiate a principled study of how the generalization
properties of approximate differential privacy can be used to perform adaptive
hypothesis testing, while giving statistically valid $p$-value corrections. We
do this by observing that the guarantees of algorithms with bounded approximate
max-inform... | computer science |
32,152 | Theoretically-Grounded Policy Advice from Multiple Teachers in
Reinforcement Learning Settings with Applications to Negative Transfer | cs.LG | Policy advice is a transfer learning method where a student agent is able to
learn faster via advice from a teacher. However, both this and other
reinforcement learning transfer methods have little theoretical analysis. This
paper formally defines a setting where multiple teacher agents can provide
advice to a student ... | computer science |
32,153 | Multi-Source Multi-View Clustering via Discrepancy Penalty | cs.LG | With the advance of technology, entities can be observed in multiple views.
Multiple views containing different types of features can be used for
clustering. Although multi-view clustering has been successfully applied in
many applications, the previous methods usually assume the complete instance
mapping between diffe... | computer science |
32,154 | Modeling Electrical Daily Demand in Presence of PHEVs in Smart Grids
with Supervised Learning | cs.LG | Replacing a portion of current light duty vehicles (LDV) with plug-in hybrid
electric vehicles (PHEVs) offers the possibility to reduce the dependence on
petroleum fuels together with environmental and economic benefits. The charging
activity of PHEVs will certainly introduce new load to the power grid. In the
framewor... | computer science |
32,155 | Mahalanobis Distance Metric Learning Algorithm for Instance-based Data
Stream Classification | cs.LG | With the massive data challenges nowadays and the rapid growing of
technology, stream mining has recently received considerable attention. To
address the large number of scenarios in which this phenomenon manifests itself
suitable tools are required in various research fields. Instance-based data
stream algorithms gene... | computer science |
32,156 | Risk-Averse Multi-Armed Bandit Problems under Mean-Variance Measure | cs.LG | The multi-armed bandit problems have been studied mainly under the measure of
expected total reward accrued over a horizon of length $T$. In this paper, we
address the issue of risk in multi-armed bandit problems and develop parallel
results under the measure of mean-variance, a commonly adopted risk measure in
economi... | computer science |
32,157 | Comparative Study of Instance Based Learning and Back Propagation for
Classification Problems | cs.LG | The paper presents a comparative study of the performance of Back Propagation
and Instance Based Learning Algorithm for classification tasks. The study is
carried out by a series of experiments will all possible combinations of
parameter values for the algorithms under evaluation. The algorithm's
classification accurac... | computer science |
32,158 | Greedy Criterion in Orthogonal Greedy Learning | cs.LG | Orthogonal greedy learning (OGL) is a stepwise learning scheme that starts
with selecting a new atom from a specified dictionary via the steepest gradient
descent (SGD) and then builds the estimator through orthogonal projection. In
this paper, we find that SGD is not the unique greedy criterion and introduce a
new gre... | computer science |
32,159 | Embedded all relevant feature selection with Random Ferns | cs.LG | Many machine learning methods can produce variable importance scores
expressing the usability of each feature in context of the produced model;
those scores on their own are yet not sufficient to generate feature selection,
especially when an all relevant selection is required. Although there are
wrapper methods aiming... | computer science |
32,160 | Nonextensive information theoretical machine | cs.LG | In this paper, we propose a new discriminative model named \emph{nonextensive
information theoretical machine (NITM)} based on nonextensive generalization of
Shannon information theory. In NITM, weight parameters are treated as random
variables. Tsallis divergence is used to regularize the distribution of weight
parame... | computer science |
32,161 | The Extended Littlestone's Dimension for Learning with Mistakes and
Abstentions | cs.LG | This paper studies classification with an abstention option in the online
setting. In this setting, examples arrive sequentially, the learner is given a
hypothesis class $\mathcal H$, and the goal of the learner is to either predict
a label on each example or abstain, while ensuring that it does not make more
than a pr... | computer science |
32,162 | Training Deep Nets with Sublinear Memory Cost | cs.LG | We propose a systematic approach to reduce the memory consumption of deep
neural network training. Specifically, we design an algorithm that costs
O(sqrt(n)) memory to train a n layer network, with only the computational cost
of an extra forward pass per mini-batch. As many of the state-of-the-art models
hit the upper ... | computer science |
32,163 | Clustering with Missing Features: A Penalized Dissimilarity Measure
based approach | cs.LG | Many real-world clustering problems are plagued by incomplete data
characterized by missing or absent features for some or all of the data
instances. Traditional clustering methods cannot be directly applied to such
data without preprocessing by imputation or marginalization techniques. In this
article, we put forth th... | computer science |
32,164 | Entity Embeddings of Categorical Variables | cs.LG | We map categorical variables in a function approximation problem into
Euclidean spaces, which are the entity embeddings of the categorical variables.
The mapping is learned by a neural network during the standard supervised
training process. Entity embedding not only reduces memory usage and speeds up
neural networks c... | computer science |
32,165 | On the Sample Complexity of End-to-end Training vs. Semantic Abstraction
Training | cs.LG | We compare the end-to-end training approach to a modular approach in which a
system is decomposed into semantically meaningful components. We focus on the
sample complexity aspect, in the regime where an extremely high accuracy is
necessary, as is the case in autonomous driving applications. We demonstrate
cases in whi... | computer science |
32,166 | Deep Learning with Eigenvalue Decay Regularizer | cs.LG | This paper extends our previous work on regularization of neural networks
using Eigenvalue Decay by employing a soft approximation of the dominant
eigenvalue in order to enable the calculation of its derivatives in relation to
the synaptic weights, and therefore the application of back-propagation, which
is a primary d... | computer science |
32,167 | Unsupervised Representation Learning of Structured Radio Communication
Signals | cs.LG | We explore unsupervised representation learning of radio communication
signals in raw sampled time series representation. We demonstrate that we can
learn modulation basis functions using convolutional autoencoders and visually
recognize their relationship to the analytic bases used in digital
communications. We also p... | computer science |
32,168 | Learning Arbitrary Sum-Product Network Leaves with
Expectation-Maximization | cs.LG | Sum-Product Networks with complex probability distribution at the leaves have
been shown to be powerful tractable-inference probabilistic models. However,
while learning the internal parameters has been amply studied, learning complex
leaf distribution is an open problem with only few results available in special
cases... | computer science |
32,169 | F-measure Maximization in Multi-Label Classification with Conditionally
Independent Label Subsets | cs.LG | We discuss a method to improve the exact F-measure maximization algorithm
called GFM, proposed in (Dembczynski et al. 2011) for multi-label
classification, assuming the label set can be can partitioned into
conditionally independent subsets given the input features. If the labels were
all independent, the estimation of... | computer science |
32,170 | MetaGrad: Multiple Learning Rates in Online Learning | cs.LG | In online convex optimization it is well known that certain subclasses of
objective functions are much easier than arbitrary convex functions. We are
interested in designing adaptive methods that can automatically get fast rates
in as many such subclasses as possible, without any manual tuning. Previous
adaptive method... | computer science |
32,171 | Contextual Bandit Learning with Predictable Rewards | cs.LG | Contextual bandit learning is a reinforcement learning problem where the
learner repeatedly receives a set of features (context), takes an action and
receives a reward based on the action and context. We consider this problem
under a realizability assumption: there exists a function in a (known) function
class, always ... | computer science |
32,172 | On the Performance of Maximum Likelihood Inverse Reinforcement Learning | cs.LG | Inverse reinforcement learning (IRL) addresses the problem of recovering a
task description given a demonstration of the optimal policy used to solve such
a task. The optimal policy is usually provided by an expert or teacher, making
IRL specially suitable for the problem of apprenticeship learning. The task
descriptio... | computer science |
32,173 | PAC Bounds for Discounted MDPs | cs.LG | We study upper and lower bounds on the sample-complexity of learning
near-optimal behaviour in finite-state discounted Markov Decision Processes
(MDPs). For the upper bound we make the assumption that each action leads to at
most two possible next-states and prove a new bound for a UCRL-style algorithm
on the number of... | computer science |
32,174 | Confusion Matrix Stability Bounds for Multiclass Classification | cs.LG | In this paper, we provide new theoretical results on the generalization
properties of learning algorithms for multiclass classification problems. The
originality of our work is that we propose to use the confusion matrix of a
classifier as a measure of its quality; our contribution is in the line of work
which attempts... | computer science |
32,175 | An Optimization Framework for Semi-Supervised and Transfer Learning
using Multiple Classifiers and Clusterers | cs.LG | Unsupervised models can provide supplementary soft constraints to help
classify new, "target" data since similar instances in the target set are more
likely to share the same class label. Such models can also help detect possible
differences between training and target distributions, which is useful in
applications whe... | computer science |
32,176 | Comparison of the C4.5 and a Naive Bayes Classifier for the Prediction
of Lung Cancer Survivability | cs.LG | Numerous data mining techniques have been developed to extract information
and identify patterns and predict trends from large data sets. In this study,
two classification techniques, the J48 implementation of the C4.5 algorithm and
a Naive Bayes classifier are applied to predict lung cancer survivability from
an exten... | computer science |
32,177 | Cumulative Step-size Adaptation on Linear Functions: Technical Report | cs.LG | The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation,
where the step size is adapted measuring the length of a so-called cumulative
path. The cumulative path is a combination of the previous steps realized by
the algorithm, where the importance of each step decreases with time. This
article studies ... | computer science |
32,178 | Communication-Efficient Parallel Belief Propagation for Latent Dirichlet
Allocation | cs.LG | This paper presents a novel communication-efficient parallel belief
propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA).
Based on the synchronous belief propagation (BP) algorithm, we first develop a
parallel belief propagation (PBP) algorithm on the parallel architecture.
Because the extensiv... | computer science |
32,179 | Clustered Bandits | cs.LG | We consider a multi-armed bandit setting that is inspired by real-world
applications in e-commerce. In our setting, there are a few types of users,
each with a specific response to the different arms. When a user enters the
system, his type is unknown to the decision maker. The decision maker can
either treat each user... | computer science |
32,180 | Exact Soft Confidence-Weighted Learning | cs.LG | In this paper, we propose a new Soft Confidence-Weighted (SCW) online
learning scheme, which enables the conventional confidence-weighted learning
method to handle non-separable cases. Unlike the previous confidence-weighted
learning algorithms, the proposed soft confidence-weighted learning method
enjoys all the four ... | computer science |
32,181 | Inductive Kernel Low-rank Decomposition with Priors: A Generalized
Nystrom Method | cs.LG | Low-rank matrix decomposition has gained great popularity recently in scaling
up kernel methods to large amounts of data. However, some limitations could
prevent them from working effectively in certain domains. For example, many
existing approaches are intrinsically unsupervised, which does not incorporate
side inform... | computer science |
32,182 | Path Integral Policy Improvement with Covariance Matrix Adaptation | cs.LG | There has been a recent focus in reinforcement learning on addressing
continuous state and action problems by optimizing parameterized policies. PI2
is a recent example of this approach. It combines a derivation from first
principles of stochastic optimal control with tools from statistical estimation
theory. In this p... | computer science |
32,183 | Optimizing F-measure: A Tale of Two Approaches | cs.LG | F-measures are popular performance metrics, particularly for tasks with
imbalanced data sets. Algorithms for learning to maximize F-measures follow two
approaches: the empirical utility maximization (EUM) approach learns a
classifier having optimal performance on training data, while the
decision-theoretic approach lea... | computer science |
32,184 | Multiple Kernel Learning from Noisy Labels by Stochastic Programming | cs.LG | We study the problem of multiple kernel learning from noisy labels. This is
in contrast to most of the previous studies on multiple kernel learning that
mainly focus on developing efficient algorithms and assume perfectly labeled
training examples. Directly applying the existing multiple kernel learning
algorithms to n... | computer science |
32,185 | Efficient Decomposed Learning for Structured Prediction | cs.LG | Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which ... | computer science |
32,186 | Two-Manifold Problems with Applications to Nonlinear System
Identification | cs.LG | Recently, there has been much interest in spectral approaches to learning
manifolds---so-called kernel eigenmap methods. These methods have had some
successes, but their applicability is limited because they are not robust to
noise. To address this limitation, we look at two-manifold problems, in which
we simultaneousl... | computer science |
32,187 | Modelling transition dynamics in MDPs with RKHS embeddings | cs.LG | We propose a new, nonparametric approach to learning and representing
transition dynamics in Markov decision processes (MDPs), which can be combined
easily with dynamic programming methods for policy optimisation and value
estimation. This approach makes use of a recently developed representation of
conditional distrib... | computer science |
32,188 | Learning with Augmented Features for Heterogeneous Domain Adaptation | cs.LG | We propose a new learning method for heterogeneous domain adaptation (HDA),
in which the data from the source domain and the target domain are represented
by heterogeneous features with different dimensions. Using two different
projection matrices, we first transform the data from two domains into a common
subspace in ... | computer science |
32,189 | Marginalized Denoising Autoencoders for Domain Adaptation | cs.LG | Stacked denoising autoencoders (SDAs) have been successfully used to learn
new representations for domain adaptation. Recently, they have attained record
accuracy on standard benchmark tasks of sentiment analysis across different
text domains. SDAs learn robust data representations by reconstruction,
recovering origina... | computer science |
32,190 | Dynamic Pricing under Finite Space Demand Uncertainty: A Multi-Armed
Bandit with Dependent Arms | cs.LG | We consider a dynamic pricing problem under unknown demand models. In this
problem a seller offers prices to a stream of customers and observes either
success or failure in each sale attempt. The underlying demand model is unknown
to the seller and can take one of N possible forms. In this paper, we show that
this prob... | computer science |
32,191 | Practical recommendations for gradient-based training of deep
architectures | cs.LG | Learning algorithms related to artificial neural networks and in particular
for Deep Learning may seem to involve many bells and whistles, called
hyper-parameters. This chapter is meant as a practical guide with
recommendations for some of the most commonly used hyper-parameters, in
particular in the context of learnin... | computer science |
32,192 | Representation Learning: A Review and New Perspectives | cs.LG | The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design represen... | computer science |
32,193 | Graph Based Classification Methods Using Inaccurate External Classifier
Information | cs.LG | In this paper we consider the problem of collectively classifying entities
where relational information is available across the entities. In practice
inaccurate class distribution for each entity is often available from another
(external) classifier. For example this distribution could come from a
classifier built usin... | computer science |
32,194 | Learning Neighborhoods for Metric Learning | cs.LG | Metric learning methods have been shown to perform well on different learning
tasks. Many of them rely on target neighborhood relationships that are computed
in the original feature space and remain fixed throughout learning. As a
result, the learned metric reflects the original neighborhood relations. We
propose a nov... | computer science |
32,195 | Advances in Optimizing Recurrent Networks | cs.LG | After a more than decade-long period of relatively little research activity
in the area of recurrent neural networks, several new developments will be
reviewed here that have allowed substantial progress both in understanding and
in technical solutions towards more efficient training of recurrent networks.
These advanc... | computer science |
32,196 | High-dimensional sequence transduction | cs.LG | We investigate the problem of transforming an input sequence into a
high-dimensional output sequence in order to transcribe polyphonic audio music
into symbolic notation. We introduce a probabilistic model based on a recurrent
neural network that is able to learn realistic output distributions given the
input and we de... | computer science |
32,197 | Cost-Sensitive Feature Selection of Data with Errors | cs.LG | In data mining applications, feature selection is an essential process since
it reduces a model's complexity. The cost of obtaining the feature values must
be taken into consideration in many domains. In this paper, we study the
cost-sensitive feature selection problem on numerical data with measurement
errors, test co... | computer science |
32,198 | Learning efficient sparse and low rank models | cs.LG | Parsimony, including sparsity and low rank, has been shown to successfully
model data in numerous machine learning and signal processing tasks.
Traditionally, such modeling approaches rely on an iterative algorithm that
minimizes an objective function with parsimony-promoting terms. The inherently
sequential structure ... | computer science |
32,199 | Analysis of Large-scale Traffic Dynamics using Non-negative Tensor
Factorization | cs.LG | In this paper, we present our work on clustering and prediction of temporal
dynamics of global congestion configurations in large-scale road networks.
Instead of looking into temporal traffic state variation of individual links,
or of small areas, we focus on spatial congestion configurations of the whole
network. In o... | computer science |
32,200 | Hybrid Fuzzy-ART based K-Means Clustering Methodology to Cellular
Manufacturing Using Operational Time | cs.LG | This paper presents a new hybrid Fuzzy-ART based K-Means Clustering technique
to solve the part machine grouping problem in cellular manufacturing systems
considering operational time. The performance of the proposed technique is
tested with problems from open literature and the results are compared to the
existing clu... | computer science |
32,201 | ADADELTA: An Adaptive Learning Rate Method | cs.LG | We present a novel per-dimension learning rate method for gradient descent
called ADADELTA. The method dynamically adapts over time using only first order
information and has minimal computational overhead beyond vanilla stochastic
gradient descent. The method requires no manual tuning of a learning rate and
appears ro... | computer science |
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