Publication

Prior Knowledge in Kernel Methods

Related publications (174)

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.

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

Automatic genre classification of music content

Giorgio Zoia, Nicolas Scaringella

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with ...

2006

A Kernel Classifier for Distributions

Samy Bengio, Alexei Pozdnoukhov

This paper presents a new algorithm for classifying distributions. The algorithm combines the principle of margin maximization and a kernel trick, applied to distributions. Thus, it combines the discriminative power of support vector machines and the well- ...

IDIAP2005

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

Application of the Evidence Framework to Brain-Computer Interfaces

Touradj Ebrahimi, Ulrich Hoffmann

A brain-computer interface (BCI) is a communication system, that implements the principle of "think and make it happen without any physical effort". This means a BCI allows a user to act on his environment only by using his thoughts, without using peripher ...

IEEE2004

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

Gaussian Processes for Machine Learning

Matthias Seeger

Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical an ...

World Scientific Publishing2004

A Localization/Verification Scheme for Finding Text in Images and Video Frames Based on Contrast Independent Features and Machine Learning Methods

Jean-Philippe Thiran, Jean-Marc Odobez, Datong Chen

Automatic character detection in video sequences is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a localization/verification scheme. Candidate text ...

2004

Adaptive Kernel Matching Pursuit for Pattern Classification

Jean-Philippe Thiran, Vlad Popovici

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the numb ...

IEEE2004

Tangent Vector Kernels for Invariant Image Classification with SVMs

Samy Bengio, Alexei Pozdnoukhov

This paper presents an application of the general sample-to-object approach to the problem of invariant image classification. The approach results in defining new SVM kernels based on tangent vectors that take into account prior information on known invari ...

2004