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PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification

Publications associées (49)

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Information theoretic combination of pattern classifiers

Jean-Philippe Thiran, Julien Meynet

Combining several classifiers has proved to be an effective machine learning technique. Two concepts clearly influence the performances of an ensemble of classifiers: the diversity between classifiers and the individual accuracies of the classifiers. In th ...

Elsevier2010

Bayesian Feature Selection Applied In a P300 Brain- Computer Interface

Touradj Ebrahimi, Jean-Marc Vesin, Ashkan Yazdani, Ulrich Hoffmann

Feature selection is a machine learning technique that has many interesting applications in the area of brain- computer interfaces (BCIs). Here we show how automatic relevance determination (ARD), which is a Bayesian feature selection technique, can be app ...

2008

SimpleMKL

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor in supervised learning settings. For the support vector machine, an efficient and general multiple kernel learning algorithm, based on semi-infinite linear ...

2008

Classification of dog barks: a machine learning approach

Frédéric Kaplan

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as ...

2008

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...

Massachusetts Institute of Technology Press2008

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the a ...

2008

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

IDIAP2007

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

Selection experiments with dynamic combinatorial libraries: the importance of the target concentration

Kay Severin

Experimental data are presented which demonstrate that the target concentration has a strong influence on the outcome of selection experiments with a dynamic mixture of self-assembled receptors. ...

2005