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Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

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Melanoma Recognition Using Representative and Discriminative Kernel Classifiers

Barbara Caputo, Tatiana Tommasi

Malignant melanoma is the most deadly for of skin lesion. Early diagnosis is of critical importance to patient survival. Visual recognition algorithms could potentially be of great help for physicians in a computer assisted diagnosis system. Previous work ...

2006

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

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 powerful kernel-based models to large datasets. We present a general framework based on t ...

2006

Interfaces of the one-dimensional voter models

Samir Brahim Belhaouari

We show that for a voter model on {0,1}Z corresponding to a random walk with kernel p(·) and starting from unanimity to the right and opposing unanimity to the left, a tight interface between 0's and 1's exists if p(·) has second moments but does not if p( ...

EPFL2006

Melanoma Recognition using Kernel Classifiers

Barbara Caputo, Tatiana Tommasi

Melanoma is the most deadly skin cancer. Early diagnosis is a current challenge for clinicians. Current algorithms for skin lesions classification focus mostly on segmentation and feature extraction. This paper instead puts the emphasis on the learning pro ...

IDIAP2006

Kernel Methods for Melanoma Recognition

Barbara Caputo, Tatiana Tommasi

2006

An integral-equation technique for solving thick irises in rectangular waveguides

Juan Ramon Mosig, Ivica Stevanovic, Pedro Crespo Valero

This paper presents an efficient integral-equation (IE) technique for analysis of thick irises inside multilayered rectangular waveguides. The IEs remain the same as in the case of zero-thickness iris and the thickness is accounted for only as a correction ...

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

Multivariate Nonlinear Regression with Semiparametric Latent Factor Models

Matthias Seeger

We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...

2005

A Kernel Trick For Sequences Applied to Text-Independent Speaker Verification Systems

Samy Bengio

This paper present a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also pro ...

IDIAP2005

Analysis of a Synonymy Network

Paolo De Los Rios, Jean-Cédric Chappelier, David Gfeller

The purpose of this report is to present the study of a linguistic network based on the relation of synonymy. The network has been extracted from a dictionary of synonyms in French. Due to errors and ambiguities in the data, the synonyms are not always gro ...

2004