PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Hierarchic Bayesian Models for Kernel Learning.
Mark Girolami and Simon Rogers
In: 22nd International Conference on Machine Learning, 7-11 Aug 2005, Bonn.

Abstract

The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes estimators for regression and classification. Illustrative experiments demonstrate the utility of the proposed method. Matlab code replicating results reported is available at http://www.dcs.gla.ac.uk/~srogers/kernel_comb.html.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1048
Deposited By:Mark Girolami
Deposited On:12 August 2005