Publication

Adaptive Kernel Matching Pursuit for Pattern Classification

Related publications (32)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Probabilistic models for multi-classifier biometric authentication using quality measures

Jonas Richiardi

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

EPFL2008

Confidence-based Cue Integration for Visual Place Recognition

Barbara Caputo

A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a give task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents an algorithm for recognition able to m ...

IDIAP2007

Confidence-based Cue Integration for Visual Place Recognition

Barbara Caputo

A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measu ...

2007

Information theoretic combination of classifiers with application to face detection

Julien Meynet

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...

EPFL2007

On the Use of A Priori Information for Sparse Signal Approximations

Pierre Vandergheynst, Lorenzo Granai, Oscar Divorra Escoda

Recent results have underlined the importance of incoherence in redundant dictionaries for a good behavior of decomposition algorithms like Matching and Basis Pursuits. However, appropriate dictionaries for a given application may not necessarily be able t ...

2006

Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

École Polytechnique Fédérale de Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Distributed SVM applied to image classification

Pascal Frossard, Effrosyni Kokiopoulou

This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of at ...

2006

Kernel-based classifiers with applications to face detection

Vlad Popovici

Statistical pattern recognition occupies a central place in the general context of machine learning techniques, as it provides the theoretical insights and the practical means for solving a variety of problems ranging from character recognition to face rec ...

EPFL2004

Sparse Gaussian Process Classification With Multiple Classes

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binar ...

Department of Statistics, University of Berkeley, CA2004