title stringlengths 5 246 | categories stringlengths 5 94 ⌀ | abstract stringlengths 54 5.03k | authors stringlengths 0 6.72k | doi stringlengths 12 54 ⌀ | id stringlengths 6 10 ⌀ | year float64 2.02k 2.02k ⌀ | venue stringclasses 13
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Learning from compressed observations | cs.IT cs.LG math.IT | The problem of statistical learning is to construct a predictor of a random
variable $Y$ as a function of a related random variable $X$ on the basis of an
i.i.d. training sample from the joint distribution of $(X,Y)$. Allowable
predictors are drawn from some specified class, and the goal is to approach
asymptotically... | Maxim Raginsky | 10.1109/ITW.2007.4313111 | 0704.0671 | null | null |
Sensor Networks with Random Links: Topology Design for Distributed
Consensus | cs.IT cs.LG math.IT | In a sensor network, in practice, the communication among sensors is subject
to:(1) errors or failures at random times; (3) costs; and(2) constraints since
sensors and networks operate under scarce resources, such as power, data rate,
or communication. The signal-to-noise ratio (SNR) is usually a main factor in
deter... | Soummya Kar and Jose M. F. Moura | 10.1109/TSP.2008.920143 | 0704.0954 | null | null |
The on-line shortest path problem under partial monitoring | cs.LG cs.SC | The on-line shortest path problem is considered under various models of
partial monitoring. Given a weighted directed acyclic graph whose edge weights
can change in an arbitrary (adversarial) way, a decision maker has to choose in
each round of a game a path between two distinguished vertices such that the
loss of th... | Andras Gyorgy, Tamas Linder, Gabor Lugosi, Gyorgy Ottucsak | null | 0704.1020 | null | null |
A neural network approach to ordinal regression | cs.LG cs.AI cs.NE | Ordinal regression is an important type of learning, which has properties of
both classification and regression. Here we describe a simple and effective
approach to adapt a traditional neural network to learn ordinal categories. Our
approach is a generalization of the perceptron method for ordinal regression.
On seve... | Jianlin Cheng | null | 0704.1028 | null | null |
Parametric Learning and Monte Carlo Optimization | cs.LG | This paper uncovers and explores the close relationship between Monte Carlo
Optimization of a parametrized integral (MCO), Parametric machine-Learning
(PL), and `blackbox' or `oracle'-based optimization (BO). We make four
contributions. First, we prove that MCO is mathematically identical to a broad
class of PL probl... | David H. Wolpert and Dev G. Rajnarayan | null | 0704.1274 | null | null |
Preconditioned Temporal Difference Learning | cs.LG cs.AI | This paper has been withdrawn by the author. This draft is withdrawn for its
poor quality in english, unfortunately produced by the author when he was just
starting his science route. Look at the ICML version instead:
http://icml2008.cs.helsinki.fi/papers/111.pdf
| Yao HengShuai | null | 0704.1409 | null | null |
A Note on the Inapproximability of Correlation Clustering | cs.LG cs.DS | We consider inapproximability of the correlation clustering problem defined
as follows: Given a graph $G = (V,E)$ where each edge is labeled either "+"
(similar) or "-" (dissimilar), correlation clustering seeks to partition the
vertices into clusters so that the number of pairs correctly (resp.
incorrectly) classifi... | Jinsong Tan | null | 0704.2092 | null | null |
Joint universal lossy coding and identification of stationary mixing
sources | cs.IT cs.LG math.IT | The problem of joint universal source coding and modeling, treated in the
context of lossless codes by Rissanen, was recently generalized to fixed-rate
lossy coding of finitely parametrized continuous-alphabet i.i.d. sources. We
extend these results to variable-rate lossy block coding of stationary ergodic
sources an... | Maxim Raginsky | null | 0704.2644 | null | null |
Supervised Feature Selection via Dependence Estimation | cs.LG | We introduce a framework for filtering features that employs the
Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence
between the features and the labels. The key idea is that good features should
maximise such dependence. Feature selection for various supervised learning
problems (including class... | Le Song, Alex Smola, Arthur Gretton, Karsten Borgwardt, Justin Bedo | null | 0704.2668 | null | null |
Equivalence of LP Relaxation and Max-Product for Weighted Matching in
General Graphs | cs.IT cs.AI cs.LG cs.NI math.IT | Max-product belief propagation is a local, iterative algorithm to find the
mode/MAP estimate of a probability distribution. While it has been successfully
employed in a wide variety of applications, there are relatively few
theoretical guarantees of convergence and correctness for general loopy graphs
that may have m... | Sujay Sanghavi | null | 0705.0760 | null | null |
HMM Speaker Identification Using Linear and Non-linear Merging
Techniques | cs.LG | Speaker identification is a powerful, non-invasive and in-expensive biometric
technique. The recognition accuracy, however, deteriorates when noise levels
affect a specific band of frequency. In this paper, we present a sub-band based
speaker identification that intends to improve the live testing performance.
Each f... | Unathi Mahola, Fulufhelo V. Nelwamondo, Tshilidzi Marwala | null | 0705.1585 | null | null |
Statistical Mechanics of Nonlinear On-line Learning for Ensemble
Teachers | cs.LG cond-mat.dis-nn | We analyze the generalization performance of a student in a model composed of
nonlinear perceptrons: a true teacher, ensemble teachers, and the student. We
calculate the generalization error of the student analytically or numerically
using statistical mechanics in the framework of on-line learning. We treat two
well-... | Hideto Utsumi, Seiji Miyoshi, Masato Okada | 10.1143/JPSJ.76.114001 | 0705.2318 | null | null |
On the monotonization of the training set | cs.LG cs.AI | We consider the problem of minimal correction of the training set to make it
consistent with monotonic constraints. This problem arises during analysis of
data sets via techniques that require monotone data. We show that this problem
is NP-hard in general and is equivalent to finding a maximal independent set in
spec... | Rustem Takhanov | null | 0705.2765 | null | null |
Mixed membership stochastic blockmodels | stat.ME cs.LG math.ST physics.soc-ph stat.ML stat.TH | Observations consisting of measurements on relationships for pairs of objects
arise in many settings, such as protein interaction and gene regulatory
networks, collections of author-recipient email, and social networks. Analyzing
such data with probabilisic models can be delicate because the simple
exchangeability as... | Edoardo M Airoldi, David M Blei, Stephen E Fienberg, Eric P Xing | null | 0705.4485 | null | null |
Loop corrections for message passing algorithms in continuous variable
models | cs.AI cs.LG | In this paper we derive the equations for Loop Corrected Belief Propagation
on a continuous variable Gaussian model. Using the exactness of the averages
for belief propagation for Gaussian models, a different way of obtaining the
covariances is found, based on Belief Propagation on cavity graphs. We discuss
the relat... | Bastian Wemmenhove and Bert Kappen | null | 0705.4566 | null | null |
A Novel Model of Working Set Selection for SMO Decomposition Methods | cs.LG cs.AI | In the process of training Support Vector Machines (SVMs) by decomposition
methods, working set selection is an important technique, and some exciting
schemes were employed into this field. To improve working set selection, we
propose a new model for working set selection in sequential minimal
optimization (SMO) deco... | Zhendong Zhao, Lei Yuan, Yuxuan Wang, Forrest Sheng Bao, Shunyi Zhang
Yanfei Sun | 10.1109/ICTAI.2007.99 | 0706.0585 | null | null |
Getting started in probabilistic graphical models | q-bio.QM cs.LG physics.soc-ph stat.ME stat.ML | Probabilistic graphical models (PGMs) have become a popular tool for
computational analysis of biological data in a variety of domains. But, what
exactly are they and how do they work? How can we use PGMs to discover patterns
that are biologically relevant? And to what extent can PGMs help us formulate
new hypotheses... | Edoardo M Airoldi | 10.1371/journal.pcbi.0030252 | 0706.2040 | null | null |
A tutorial on conformal prediction | cs.LG stat.ML | Conformal prediction uses past experience to determine precise levels of
confidence in new predictions. Given an error probability $\epsilon$, together
with a method that makes a prediction $\hat{y}$ of a label $y$, it produces a
set of labels, typically containing $\hat{y}$, that also contains $y$ with
probability $... | Glenn Shafer and Vladimir Vovk | null | 0706.3188 | null | null |
The Role of Time in the Creation of Knowledge | cs.LG cs.AI cs.IT math.IT | This paper I assume that in humans the creation of knowledge depends on a
discrete time, or stage, sequential decision-making process subjected to a
stochastic, information transmitting environment. For each time-stage, this
environment randomly transmits Shannon type information-packets to the
decision-maker, who ex... | Roy E. Murphy | null | 0707.0498 | null | null |
Clustering and Feature Selection using Sparse Principal Component
Analysis | cs.AI cs.LG cs.MS | In this paper, we study the application of sparse principal component
analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks
sparse factors, or linear combinations of the data variables, explaining a
maximum amount of variance in the data while having only a limited number of
nonzero coefficien... | Ronny Luss, Alexandre d'Aspremont | null | 0707.0701 | null | null |
Model Selection Through Sparse Maximum Likelihood Estimation | cs.AI cs.LG | We consider the problem of estimating the parameters of a Gaussian or binary
distribution in such a way that the resulting undirected graphical model is
sparse. Our approach is to solve a maximum likelihood problem with an added
l_1-norm penalty term. The problem as formulated is convex but the memory
requirements an... | Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont | null | 0707.0704 | null | null |
Optimal Solutions for Sparse Principal Component Analysis | cs.AI cs.LG | Given a sample covariance matrix, we examine the problem of maximizing the
variance explained by a linear combination of the input variables while
constraining the number of nonzero coefficients in this combination. This is
known as sparse principal component analysis and has a wide array of
applications in machine l... | Alexandre d'Aspremont, Francis Bach, Laurent El Ghaoui | null | 0707.0705 | null | null |
A New Generalization of Chebyshev Inequality for Random Vectors | math.ST cs.LG math.PR stat.AP stat.TH | In this article, we derive a new generalization of Chebyshev inequality for
random vectors. We demonstrate that the new generalization is much less
conservative than the classical generalization.
| Xinjia Chen | null | 0707.0805 | null | null |
Clusters, Graphs, and Networks for Analysing Internet-Web-Supported
Communication within a Virtual Community | cs.AI cs.LG | The proposal is to use clusters, graphs and networks as models in order to
analyse the Web structure. Clusters, graphs and networks provide knowledge
representation and organization. Clusters were generated by co-site analysis.
The sample is a set of academic Web sites from the countries belonging to the
European Uni... | Xavier Polanco (INIST) | null | 0707.1452 | null | null |
Universal Reinforcement Learning | cs.IT cs.LG math.IT | We consider an agent interacting with an unmodeled environment. At each time,
the agent makes an observation, takes an action, and incurs a cost. Its actions
can influence future observations and costs. The goal is to minimize the
long-term average cost. We propose a novel algorithm, known as the active LZ
algorithm,... | Vivek F. Farias, Ciamac C. Moallemi, Tsachy Weissman, Benjamin Van Roy | null | 0707.3087 | null | null |
Consistency of the group Lasso and multiple kernel learning | cs.LG | We consider the least-square regression problem with regularization by a
block 1-norm, i.e., a sum of Euclidean norms over spaces of dimensions larger
than one. This problem, referred to as the group Lasso, extends the usual
regularization by the 1-norm where all spaces have dimension one, where it is
commonly referr... | Francis Bach (WILLOW Project - Inria/Ens) | null | 0707.3390 | null | null |
Quantum Algorithms for Learning and Testing Juntas | quant-ph cs.LG | In this article we develop quantum algorithms for learning and testing
juntas, i.e. Boolean functions which depend only on an unknown set of k out of
n input variables. Our aim is to develop efficient algorithms:
- whose sample complexity has no dependence on n, the dimension of the domain
the Boolean functions are... | Alp Atici, Rocco A. Servedio | 10.1007/s11128-007-0061-6 | 0707.3479 | null | null |
Virtual screening with support vector machines and structure kernels | q-bio.QM cs.LG | Support vector machines and kernel methods have recently gained considerable
attention in chemoinformatics. They offer generally good performance for
problems of supervised classification or regression, and provide a flexible and
computationally efficient framework to include relevant information and prior
knowledge ... | Pierre Mah\'e (XRCE), Jean-Philippe Vert (CB) | null | 0708.0171 | null | null |
Structure or Noise? | physics.data-an cond-mat.stat-mech cs.IT cs.LG math-ph math.IT math.MP math.ST nlin.CD stat.TH | We show how rate-distortion theory provides a mechanism for automated theory
building by naturally distinguishing between regularity and randomness. We
start from the simple principle that model variables should, as much as
possible, render the future and past conditionally independent. From this, we
construct an obj... | Susanne Still, James P. Crutchfield | null | 0708.0654 | null | null |
Cost-minimising strategies for data labelling : optimal stopping and
active learning | cs.LG | Supervised learning deals with the inference of a distribution over an output
or label space $\CY$ conditioned on points in an observation space $\CX$, given
a training dataset $D$ of pairs in $\CX \times \CY$. However, in a lot of
applications of interest, acquisition of large amounts of observations is easy,
while ... | Christos Dimitrakakis and Christian Savu-Krohn | null | 0708.1242 | null | null |
Defensive forecasting for optimal prediction with expert advice | cs.LG | The method of defensive forecasting is applied to the problem of prediction
with expert advice for binary outcomes. It turns out that defensive forecasting
is not only competitive with the Aggregating Algorithm but also handles the
case of "second-guessing" experts, whose advice depends on the learner's
prediction; t... | Vladimir Vovk | null | 0708.1503 | null | null |
Optimal Causal Inference: Estimating Stored Information and
Approximating Causal Architecture | cs.IT cond-mat.stat-mech cs.LG math.IT math.ST stat.TH | We introduce an approach to inferring the causal architecture of stochastic
dynamical systems that extends rate distortion theory to use causal
shielding---a natural principle of learning. We study two distinct cases of
causal inference: optimal causal filtering and optimal causal estimation.
Filtering corresponds ... | Susanne Still, James P. Crutchfield, Christopher J. Ellison | null | 0708.1580 | null | null |
On Semimeasures Predicting Martin-Loef Random Sequences | cs.IT cs.LG math.IT math.PR | Solomonoff's central result on induction is that the posterior of a universal
semimeasure M converges rapidly and with probability 1 to the true sequence
generating posterior mu, if the latter is computable. Hence, M is eligible as a
universal sequence predictor in case of unknown mu. Despite some nearby results
and ... | Marcus Hutter and Andrej Muchnik | null | 0708.2319 | null | null |
Continuous and randomized defensive forecasting: unified view | cs.LG | Defensive forecasting is a method of transforming laws of probability (stated
in game-theoretic terms as strategies for Sceptic) into forecasting algorithms.
There are two known varieties of defensive forecasting: "continuous", in which
Sceptic's moves are assumed to depend on the forecasts in a (semi)continuous
mann... | Vladimir Vovk | null | 0708.2353 | null | null |
A Dichotomy Theorem for General Minimum Cost Homomorphism Problem | cs.LG cs.CC | In the constraint satisfaction problem ($CSP$), the aim is to find an
assignment of values to a set of variables subject to specified constraints. In
the minimum cost homomorphism problem ($MinHom$), one is additionally given
weights $c_{va}$ for every variable $v$ and value $a$, and the aim is to find
an assignment ... | Rustem Takhanov | null | 0708.3226 | null | null |
Filtering Additive Measurement Noise with Maximum Entropy in the Mean | cs.LG | The purpose of this note is to show how the method of maximum entropy in the
mean (MEM) may be used to improve parametric estimation when the measurements
are corrupted by large level of noise. The method is developed in the context
on a concrete example: that of estimation of the parameter in an exponential
distribu... | Henryk Gzyl and Enrique ter Horst | null | 0709.0509 | null | null |
On Universal Prediction and Bayesian Confirmation | math.ST cs.IT cs.LG math.IT stat.ML stat.TH | The Bayesian framework is a well-studied and successful framework for
inductive reasoning, which includes hypothesis testing and confirmation,
parameter estimation, sequence prediction, classification, and regression. But
standard statistical guidelines for choosing the model class and prior are not
always available ... | Marcus Hutter | null | 0709.1516 | null | null |
Learning for Dynamic Bidding in Cognitive Radio Resources | cs.LG cs.GT | In this paper, we model the various wireless users in a cognitive radio
network as a collection of selfish, autonomous agents that strategically
interact in order to acquire the dynamically available spectrum opportunities.
Our main focus is on developing solutions for wireless users to successfully
compete with each... | Fangwen Fu, Mihaela van der Schaar | null | 0709.2446 | null | null |
Mutual information for the selection of relevant variables in
spectrometric nonlinear modelling | cs.LG cs.NE stat.AP | Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, lead... | Fabrice Rossi (INRIA Rocquencourt / INRIA Sophia Antipolis), Amaury
Lendasse (CIS), Damien Fran\c{c}ois (CESAME), Vincent Wertz (CESAME), Michel
Verleysen (DICE - MLG) | 10.1016/j.chemolab.2005.06.010 | 0709.3427 | null | null |
Fast Algorithm and Implementation of Dissimilarity Self-Organizing Maps | cs.NE cs.LG | In many real world applications, data cannot be accurately represented by
vectors. In those situations, one possible solution is to rely on dissimilarity
measures that enable sensible comparison between observations. Kohonen's
Self-Organizing Map (SOM) has been adapted to data described only through their
dissimilari... | Brieuc Conan-Guez (LITA), Fabrice Rossi (INRIA Rocquencourt / INRIA
Sophia Antipolis), A\"icha El Golli (INRIA Rocquencourt / INRIA Sophia
Antipolis) | 10.1016/j.neunet.2006.05.002 | 0709.3461 | null | null |
Une adaptation des cartes auto-organisatrices pour des donn\'ees
d\'ecrites par un tableau de dissimilarit\'es | cs.NE cs.LG | Many data analysis methods cannot be applied to data that are not represented
by a fixed number of real values, whereas most of real world observations are
not readily available in such a format. Vector based data analysis methods have
therefore to be adapted in order to be used with non standard complex data. A
flex... | A\"icha El Golli (INRIA Rocquencourt / INRIA Sophia Antipolis),
Fabrice Rossi (INRIA Rocquencourt / INRIA Sophia Antipolis), Brieuc
Conan-Guez (LITA), Yves Lechevallier (INRIA Rocquencourt / INRIA Sophia
Antipolis) | null | 0709.3586 | null | null |
Self-organizing maps and symbolic data | cs.NE cs.LG | In data analysis new forms of complex data have to be considered like for
example (symbolic data, functional data, web data, trees, SQL query and
multimedia data, ...). In this context classical data analysis for knowledge
discovery based on calculating the center of gravity can not be used because
input are not $\ma... | A\"icha El Golli (INRIA Rocquencourt / INRIA Sophia Antipolis), Brieuc
Conan-Guez (INRIA Rocquencourt / INRIA Sophia Antipolis), Fabrice Rossi
(INRIA Rocquencourt / INRIA Sophia Antipolis) | null | 0709.3587 | null | null |
Fast Selection of Spectral Variables with B-Spline Compression | cs.LG stat.AP | The large number of spectral variables in most data sets encountered in
spectral chemometrics often renders the prediction of a dependent variable
uneasy. The number of variables hopefully can be reduced, by using either
projection techniques or selection methods; the latter allow for the
interpretation of the select... | Fabrice Rossi (INRIA Rocquencourt / INRIA Sophia Antipolis), Damien
Fran\c{c}ois (CESAME), Vincent Wertz (CESAME), Marc Meurens (BNUT), Michel
Verleysen (DICE - MLG) | 10.1016/j.chemolab.2006.06.007 | 0709.3639 | null | null |
Resampling methods for parameter-free and robust feature selection with
mutual information | cs.LG stat.AP | Combining the mutual information criterion with a forward feature selection
strategy offers a good trade-off between optimality of the selected feature
subset and computation time. However, it requires to set the parameter(s) of
the mutual information estimator and to determine when to halt the forward
procedure. The... | Damien Fran\c{c}ois (CESAME), Fabrice Rossi (INRIA Rocquencourt /
INRIA Sophia Antipolis), Vincent Wertz (CESAME), Michel Verleysen (DICE -
MLG) | 10.1016/j.neucom.2006.11.019 | 0709.3640 | null | null |
Evolving Classifiers: Methods for Incremental Learning | cs.LG cs.AI cs.NE | The ability of a classifier to take on new information and classes by
evolving the classifier without it having to be fully retrained is known as
incremental learning. Incremental learning has been successfully applied to
many classification problems, where the data is changing and is not all
available at once. In th... | Greg Hulley and Tshilidzi Marwala | null | 0709.3965 | null | null |
Classification of Images Using Support Vector Machines | cs.LG cs.AI | Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs... | Gidudu Anthony, Hulley Greg and Marwala Tshilidzi | null | 0709.3967 | null | null |
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 i... | Vladimir Vovk and Fedor Zhdanov | null | 0710.0485 | null | null |
Association Rules in the Relational Calculus | cs.DB cs.LG cs.LO | One of the most utilized data mining tasks is the search for association
rules. Association rules represent significant relationships between items in
transactions. We extend the concept of association rule to represent a much
broader class of associations, which we refer to as \emph{entity-relationship
rules.} Seman... | Oliver Schulte, Flavia Moser, Martin Ester and Zhiyong Lu | null | 0710.2083 | null | null |
The structure of verbal sequences analyzed with unsupervised learning
techniques | cs.CL cs.AI cs.LG | Data mining allows the exploration of sequences of phenomena, whereas one
usually tends to focus on isolated phenomena or on the relation between two
phenomena. It offers invaluable tools for theoretical analyses and exploration
of the structure of sentences, texts, dialogues, and speech. We report here the
results o... | Catherine Recanati (LIPN), Nicoleta Rogovschi (LIPN), Youn\`es Bennani
(LIPN) | null | 0710.2446 | null | null |
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 wi... | Francis Bach (WILLOW Project - Inria/Ens) | null | 0710.2848 | null | null |
An efficient reduction of ranking to classification | cs.LG cs.IR | This paper describes an efficient reduction of the learning problem of
ranking to binary classification. The reduction guarantees an average pairwise
misranking regret of at most that of the binary classifier regret, improving a
recent result of Balcan et al which only guarantees a factor of 2. Moreover,
our reductio... | Nir Ailon and Mehryar Mohri | null | 0710.2889 | null | null |
Combining haplotypers | cs.LG cs.CE q-bio.QM | Statistically resolving the underlying haplotype pair for a genotype
measurement is an important intermediate step in gene mapping studies, and has
received much attention recently. Consequently, a variety of methods for this
problem have been developed. Different methods employ different statistical
models, and thus... | Matti K\"a\"ari\"ainen, Niels Landwehr, Sampsa Lappalainen and Taneli
Mielik\"ainen | null | 0710.5116 | null | null |
A Tutorial on Spectral Clustering | cs.DS cs.LG | In recent years, spectral clustering has become one of the most popular
modern clustering algorithms. It is simple to implement, can be solved
efficiently by standard linear algebra software, and very often outperforms
traditional clustering algorithms such as the k-means algorithm. On the first
glance spectral clust... | Ulrike von Luxburg | null | 0711.0189 | null | null |
Building Rules on Top of Ontologies for the Semantic Web with Inductive
Logic Programming | cs.AI cs.LG | Building rules on top of ontologies is the ultimate goal of the logical layer
of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is
currently under discussion. It is intended to follow the tradition of hybrid
knowledge representation and reasoning systems such as $\mathcal{AL}$-log that
integr... | Francesca A. Lisi | null | 0711.1814 | null | null |
Empirical Evaluation of Four Tensor Decomposition Algorithms | cs.LG cs.CL cs.IR | Higher-order tensor decompositions are analogous to the familiar Singular
Value Decomposition (SVD), but they transcend the limitations of matrices
(second-order tensors). SVD is a powerful tool that has achieved impressive
results in information retrieval, collaborative filtering, computational
linguistics, computat... | Peter D. Turney (National Research Council of Canada) | null | 0711.2023 | null | null |
Inverse Sampling for Nonasymptotic Sequential Estimation of Bounded
Variable Means | math.ST cs.LG math.PR stat.TH | In this paper, we consider the nonasymptotic sequential estimation of means
of random variables bounded in between zero and one. We have rigorously
demonstrated that, in order to guarantee prescribed relative precision and
confidence level, it suffices to continue sampling until the sample sum is no
less than a certa... | Xinjia Chen | null | 0711.2801 | null | null |
Image Classification Using SVMs: One-against-One Vs One-against-All | cs.LG cs.AI cs.CV | Support Vector Machines (SVMs) are a relatively new supervised classification
technique to the land cover mapping community. They have their roots in
Statistical Learning Theory and have gained prominence because they are robust,
accurate and are effective even when using a small training sample. By their
nature SVMs... | Gidudu Anthony, Hulley Gregg and Marwala Tshilidzi | null | 0711.2914 | null | null |
Clustering with Transitive Distance and K-Means Duality | cs.LG | Recent spectral clustering methods are a propular and powerful technique for
data clustering. These methods need to solve the eigenproblem whose
computational complexity is $O(n^3)$, where $n$ is the number of data samples.
In this paper, a non-eigenproblem based clustering method is proposed to deal
with the cluster... | Chunjing Xu, Jianzhuang Liu, Xiaoou Tang | null | 0711.3594 | null | null |
Derivations of Normalized Mutual Information in Binary Classifications | cs.LG cs.IT math.IT | This correspondence studies the basic problem of classifications - how to
evaluate different classifiers. Although the conventional performance indexes,
such as accuracy, are commonly used in classifier selection or evaluation,
information-based criteria, such as mutual information, are becoming popular in
feature/mo... | Yong Wang, Bao-Gang Hu | null | 0711.3675 | null | null |
Covariance and PCA for Categorical Variables | cs.LG | Covariances from categorical variables are defined using a regular simplex
expression for categories. The method follows the variance definition by Gini,
and it gives the covariance as a solution of simultaneous equations. The
calculated results give reasonable values for test data. A method of principal
component an... | Hirotaka Niitsuma and Takashi Okada | null | 0711.4452 | null | null |
On the Relationship between the Posterior and Optimal Similarity | cs.LG | For a classification problem described by the joint density $P(\omega,x)$,
models of $P(\omega\eq\omega'|x,x')$ (the ``Bayesian similarity measure'') have
been shown to be an optimal similarity measure for nearest neighbor
classification. This paper analyzes demonstrates several additional properties
of that conditio... | Thomas M. Breuel | null | 0712.0130 | null | null |
A Reactive Tabu Search Algorithm for Stimuli Generation in
Psycholinguistics | cs.AI cs.CC cs.DM cs.LG | The generation of meaningless "words" matching certain statistical and/or
linguistic criteria is frequently needed for experimental purposes in
Psycholinguistics. Such stimuli receive the name of pseudowords or nonwords in
the Cognitive Neuroscience literatue. The process for building nonwords
sometimes has to be bas... | Alejandro Chinea Manrique De Lara | null | 0712.0451 | null | null |
Equations of States in Singular Statistical Estimation | cs.LG | Learning machines which have hierarchical structures or hidden variables are
singular statistical models because they are nonidentifiable and their Fisher
information matrices are singular. In singular statistical models, neither the
Bayes a posteriori distribution converges to the normal distribution nor the
maximum... | Sumio Watanabe | null | 0712.0653 | null | null |
A Universal Kernel for Learning Regular Languages | cs.LG cs.DM | We give a universal kernel that renders all the regular languages linearly
separable. We are not able to compute this kernel efficiently and conjecture
that it is intractable, but we do have an efficient $\eps$-approximation.
| Leonid (Aryeh) Kontorovich | null | 0712.0840 | null | null |
Automatic Pattern Classification by Unsupervised Learning Using
Dimensionality Reduction of Data with Mirroring Neural Networks | cs.LG cs.AI cs.NE | This paper proposes an unsupervised learning technique by using Multi-layer
Mirroring Neural Network and Forgy's clustering algorithm. Multi-layer
Mirroring Neural Network is a neural network that can be trained with
generalized data inputs (different categories of image patterns) to perform
non-linear dimensionality... | Dasika Ratna Deepthi, G.R.Aditya Krishna and K. Eswaran | null | 0712.0938 | null | null |
Reconstruction of Markov Random Fields from Samples: Some Easy
Observations and Algorithms | cs.CC cs.LG | Markov random fields are used to model high dimensional distributions in a
number of applied areas. Much recent interest has been devoted to the
reconstruction of the dependency structure from independent samples from the
Markov random fields. We analyze a simple algorithm for reconstructing the
underlying graph defi... | Guy Bresler, Elchanan Mossel, Allan Sly | null | 0712.1402 | null | null |
A New Theoretic Foundation for Cross-Layer Optimization | cs.NI cs.LG | Cross-layer optimization solutions have been proposed in recent years to
improve the performance of network users operating in a time-varying,
error-prone wireless environment. However, these solutions often rely on ad-hoc
optimization approaches, which ignore the different environmental dynamics
experienced at vario... | Fangwen Fu and Mihaela van der Schaar | null | 0712.2497 | null | null |
Density estimation in linear time | cs.LG | We consider the problem of choosing a density estimate from a set of
distributions F, minimizing the L1-distance to an unknown distribution
(Devroye, Lugosi 2001). Devroye and Lugosi analyze two algorithms for the
problem: Scheffe tournament winner and minimum distance estimate. The Scheffe
tournament estimate requir... | Satyaki Mahalanabis, Daniel Stefankovic | null | 0712.2869 | null | null |
Graph kernels between point clouds | cs.LG | Point clouds are sets of points in two or three dimensions. Most kernel
methods for learning on sets of points have not yet dealt with the specific
geometrical invariances and practical constraints associated with point clouds
in computer vision and graphics. In this paper, we present extensions of graph
kernels for ... | Francis Bach (WILLOW Project - Inria/Ens) | null | 0712.3402 | null | null |
Improving the Performance of PieceWise Linear Separation Incremental
Algorithms for Practical Hardware Implementations | cs.NE cs.AI cs.LG | In this paper we shall review the common problems associated with Piecewise
Linear Separation incremental algorithms. This kind of neural models yield poor
performances when dealing with some classification problems, due to the
evolving schemes used to construct the resulting networks. So as to avoid this
undesirable... | Alejandro Chinea Manrique De Lara, Juan Manuel Moreno, Arostegui Jordi
Madrenas, Joan Cabestany | null | 0712.3654 | null | null |
Improved Collaborative Filtering Algorithm via Information
Transformation | cs.LG cs.CY | In this paper, we propose a spreading activation approach for collaborative
filtering (SA-CF). By using the opinion spreading process, the similarity
between any users can be obtained. The algorithm has remarkably higher accuracy
than the standard collaborative filtering (CF) using Pearson correlation.
Furthermore, w... | Jian-Guo Liu, Bing-Hong Wang, Qiang Guo | 10.1142/S0129183109013613 | 0712.3807 | null | null |
Online EM Algorithm for Latent Data Models | stat.CO cs.LG | In this contribution, we propose a generic online (also sometimes called
adaptive or recursive) version of the Expectation-Maximisation (EM) algorithm
applicable to latent variable models of independent observations. Compared to
the algorithm of Titterington (1984), this approach is more directly connected
to the usu... | Olivier Capp\'e (LTCI), Eric Moulines (LTCI) | 10.1111/j.1467-9868.2009.00698.x | 0712.4273 | null | null |
Staring at Economic Aggregators through Information Lenses | cs.IT cs.LG math.IT math.OC | It is hard to exaggerate the role of economic aggregators -- functions that
summarize numerous and / or heterogeneous data -- in economic models since the
early XX$^{th}$ century. In many cases, as witnessed by the pioneering works of
Cobb and Douglas, these functions were information quantities tailored to
economic ... | Richard Nock, Nicolas Sanz, Fred Celimene, Frank Nielsen | null | 0801.0390 | null | null |
Online variants of the cross-entropy method | cs.LG | The cross-entropy method is a simple but efficient method for global
optimization. In this paper we provide two online variants of the basic CEM,
together with a proof of convergence.
| Istvan Szita and Andras Lorincz | null | 0801.1988 | null | null |
Factored Value Iteration Converges | cs.AI cs.LG | In this paper we propose a novel algorithm, factored value iteration (FVI),
for the approximate solution of factored Markov decision processes (fMDPs). The
traditional approximate value iteration algorithm is modified in two ways. For
one, the least-squares projection operator is modified so that it does not
increase... | Istvan Szita and Andras Lorincz | null | 0801.2069 | null | null |
The optimal assignment kernel is not positive definite | cs.LG | We prove that the optimal assignment kernel, proposed recently as an attempt
to embed labeled graphs and more generally tuples of basic data to a Hilbert
space, is in fact not always positive definite.
| Jean-Philippe Vert (CB) | null | 0801.4061 | null | null |
Information Width | cs.DM cs.IT cs.LG math.IT | Kolmogorov argued that the concept of information exists also in problems
with no underlying stochastic model (as Shannon's information representation)
for instance, the information contained in an algorithm or in the genome. He
introduced a combinatorial notion of entropy and information $I(x:\sy)$
conveyed by a bin... | Joel Ratsaby | null | 0801.4790 | null | null |
On the Complexity of Binary Samples | cs.DM cs.AI cs.LG | Consider a class $\mH$ of binary functions $h: X\to\{-1, +1\}$ on a finite
interval $X=[0, B]\subset \Real$. Define the {\em sample width} of $h$ on a
finite subset (a sample) $S\subset X$ as $\w_S(h) \equiv \min_{x\in S}
|\w_h(x)|$, where $\w_h(x) = h(x) \max\{a\geq 0: h(z)=h(x), x-a\leq z\leq
x+a\}$. Let $\mathbb{S... | Joel Ratsaby | null | 0801.4794 | null | null |
New Estimation Procedures for PLS Path Modelling | cs.LG | Given R groups of numerical variables X1, ... XR, we assume that each group
is the result of one underlying latent variable, and that all latent variables
are bound together through a linear equation system. Moreover, we assume that
some explanatory latent variables may interact pairwise in one or more
equations. We ... | Xavier Bry (I3M) | null | 0802.1002 | null | null |
Learning Balanced Mixtures of Discrete Distributions with Small Sample | cs.LG stat.ML | We study the problem of partitioning a small sample of $n$ individuals from a
mixture of $k$ product distributions over a Boolean cube $\{0, 1\}^K$ according
to their distributions. Each distribution is described by a vector of allele
frequencies in $\R^K$. Given two distributions, we use $\gamma$ to denote the
avera... | Shuheng Zhou | null | 0802.1244 | null | null |
Bayesian Nonlinear Principal Component Analysis Using Random Fields | cs.CV cs.LG | We propose a novel model for nonlinear dimension reduction motivated by the
probabilistic formulation of principal component analysis. Nonlinearity is
achieved by specifying different transformation matrices at different locations
of the latent space and smoothing the transformation using a Markov random
field type p... | Heng Lian | null | 0802.1258 | null | null |
A New Approach to Collaborative Filtering: Operator Estimation with
Spectral Regularization | cs.LG | We present a general approach for collaborative filtering (CF) using spectral
regularization to learn linear operators from "users" to the "objects" they
rate. Recent low-rank type matrix completion approaches to CF are shown to be
special cases. However, unlike existing regularization based CF methods, our
approach ... | Jacob Abernethy, Francis Bach (INRIA Rocquencourt), Theodoros
Evgeniou, Jean-Philippe Vert (CB) | null | 0802.1430 | null | null |
Combining Expert Advice Efficiently | cs.LG cs.DS cs.IT math.IT | We show how models for prediction with expert advice can be defined concisely
and clearly using hidden Markov models (HMMs); standard HMM algorithms can then
be used to efficiently calculate, among other things, how the expert
predictions should be weighted according to the model. We cast many existing
models as HMMs... | Wouter Koolen and Steven de Rooij | null | 0802.2015 | null | null |
A Radar-Shaped Statistic for Testing and Visualizing Uniformity
Properties in Computer Experiments | cs.LG math.ST stat.TH | In the study of computer codes, filling space as uniformly as possible is
important to describe the complexity of the investigated phenomenon. However,
this property is not conserved by reducing the dimension. Some numeric
experiment designs are conceived in this sense as Latin hypercubes or
orthogonal arrays, but th... | Jessica Franco, Laurent Carraro, Olivier Roustant, Astrid Jourdan
(LMA-PAU) | null | 0802.2158 | null | null |
Compressed Counting | cs.IT cs.CC cs.DM cs.DS cs.LG math.IT | Counting is among the most fundamental operations in computing. For example,
counting the pth frequency moment has been a very active area of research, in
theoretical computer science, databases, and data mining. When p=1, the task
(i.e., counting the sum) can be accomplished using a simple counter.
Compressed Coun... | Ping Li | null | 0802.2305 | null | null |
Sign Language Tutoring Tool | cs.LG cs.HC | In this project, we have developed a sign language tutor that lets users
learn isolated signs by watching recorded videos and by trying the same signs.
The system records the user's video and analyses it. If the sign is recognized,
both verbal and animated feedback is given to the user. The system is able to
recogniz... | Oya Aran, Ismail Ari, Alexandre Benoit (GIPSA-lab), Ana Huerta
Carrillo, Fran\c{c}ois-Xavier Fanard (TELE), Pavel Campr, Lale Akarun, Alice
Caplier (GIPSA-lab), Michele Rombaut (GIPSA-lab), Bulent Sankur | null | 0802.2428 | null | null |
Pure Exploration for Multi-Armed Bandit Problems | math.ST cs.LG stat.TH | We consider the framework of stochastic multi-armed bandit problems and study
the possibilities and limitations of forecasters that perform an on-line
exploration of the arms. These forecasters are assessed in terms of their
simple regret, a regret notion that captures the fact that exploration is only
constrained by... | S\'ebastien Bubeck (INRIA Futurs), R\'emi Munos (INRIA Futurs), Gilles
Stoltz (DMA, GREGH) | null | 0802.2655 | null | null |
Knowledge Technologies | cs.CY cs.AI cs.LG cs.SE | Several technologies are emerging that provide new ways to capture, store,
present and use knowledge. This book is the first to provide a comprehensive
introduction to five of the most important of these technologies: Knowledge
Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and
Semantic Webs. Fo... | Nick Milton | null | 0802.3789 | null | null |
What Can We Learn Privately? | cs.LG cs.CC cs.CR cs.DB | Learning problems form an important category of computational tasks that
generalizes many of the computations researchers apply to large real-life data
sets. We ask: what concept classes can be learned privately, namely, by an
algorithm whose output does not depend too heavily on any one input or specific
training ex... | Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya
Raskhodnikova, and Adam Smith | null | 0803.0924 | null | null |
Privacy Preserving ID3 over Horizontally, Vertically and Grid
Partitioned Data | cs.DB cs.LG | We consider privacy preserving decision tree induction via ID3 in the case
where the training data is horizontally or vertically distributed. Furthermore,
we consider the same problem in the case where the data is both horizontally
and vertically distributed, a situation we refer to as grid partitioned data.
We give ... | Bart Kuijpers, Vanessa Lemmens, Bart Moelans and Karl Tuyls | null | 0803.1555 | null | null |
Figuring out Actors in Text Streams: Using Collocations to establish
Incremental Mind-maps | cs.CL cs.LG | The recognition, involvement, and description of main actors influences the
story line of the whole text. This is of higher importance as the text per se
represents a flow of words and expressions that once it is read it is lost. In
this respect, the understanding of a text and moreover on how the actor exactly
behav... | T. Rothenberger, S. Oez, E. Tahirovic, C. Schommer | null | 0803.2856 | null | null |
Robustness and Regularization of Support Vector Machines | cs.LG cs.AI | We consider regularized support vector machines (SVMs) and show that they are
precisely equivalent to a new robust optimization formulation. We show that
this equivalence of robust optimization and regularization has implications for
both algorithms, and analysis. In terms of algorithms, the equivalence suggests
more... | Huan Xu, Constantine Caramanis and Shie Mannor | null | 0803.3490 | null | null |
Recorded Step Directional Mutation for Faster Convergence | cs.NE cs.LG | Two meta-evolutionary optimization strategies described in this paper
accelerate the convergence of evolutionary programming algorithms while still
retaining much of their ability to deal with multi-modal problems. The
strategies, called directional mutation and recorded step in this paper, can
operate independently ... | Ted Dunning | null | 0803.3838 | null | null |
Support Vector Machine Classification with Indefinite Kernels | cs.LG cs.AI | We propose a method for support vector machine classification using
indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex
loss function, our algorithm simultaneously computes support vectors and a
proxy kernel matrix used in forming the loss. This can be interpreted as a
penalized kernel learn... | Ronny Luss, Alexandre d'Aspremont | null | 0804.0188 | null | null |
A Unified Semi-Supervised Dimensionality Reduction Framework for
Manifold Learning | cs.LG cs.AI | We present a general framework of semi-supervised dimensionality reduction
for manifold learning which naturally generalizes existing supervised and
unsupervised learning frameworks which apply the spectral decomposition.
Algorithms derived under our framework are able to employ both labeled and
unlabeled examples an... | Ratthachat Chatpatanasiri and Boonserm Kijsirikul | null | 0804.0924 | null | null |
Bolasso: model consistent Lasso estimation through the bootstrap | cs.LG math.ST stat.ML stat.TH | We consider the least-square linear regression problem with regularization by
the l1-norm, a problem usually referred to as the Lasso. In this paper, we
present a detailed asymptotic analysis of model consistency of the Lasso. For
various decays of the regularization parameter, we compute asymptotic
equivalents of th... | Francis Bach (INRIA Rocquencourt) | null | 0804.1302 | null | null |
On Kernelization of Supervised Mahalanobis Distance Learners | cs.LG cs.AI | This paper focuses on the problem of kernelizing an existing supervised
Mahalanobis distance learner. The following features are included in the paper.
Firstly, three popular learners, namely, "neighborhood component analysis",
"large margin nearest neighbors" and "discriminant neighborhood embedding",
which do not h... | Ratthachat Chatpatanasiri, Teesid Korsrilabutr, Pasakorn
Tangchanachaianan and Boonserm Kijsirikul | null | 0804.1441 | null | null |
Isotropic PCA and Affine-Invariant Clustering | cs.LG cs.CG | We present a new algorithm for clustering points in R^n. The key property of
the algorithm is that it is affine-invariant, i.e., it produces the same
partition for any affine transformation of the input. It has strong guarantees
when the input is drawn from a mixture model. For a mixture of two arbitrary
Gaussians, t... | S. Charles Brubaker and Santosh S. Vempala | null | 0804.3575 | null | null |
Multiple Random Oracles Are Better Than One | cs.LG | We study the problem of learning k-juntas given access to examples drawn from
a number of different product distributions. Thus we wish to learn a function f
: {-1,1}^n -> {-1,1} that depends on k (unknown) coordinates. While the best
known algorithms for the general problem of learning a k-junta require running
time... | Jan Arpe and Elchanan Mossel | null | 0804.3817 | null | null |
Dependence Structure Estimation via Copula | cs.LG cs.IR stat.ME | Dependence strucuture estimation is one of the important problems in machine
learning domain and has many applications in different scientific areas. In
this paper, a theoretical framework for such estimation based on copula and
copula entropy -- the probabilistic theory of representation and measurement of
statistic... | Jian Ma and Zengqi Sun | null | 0804.4451 | null | null |
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