Hierarchic Bayesian models for kernel learning

Girolami, M. and Rogers, S. (2005) Hierarchic Bayesian models for kernel learning. In: Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 7-11 August 2005., pp. 241-248. ISBN 1595931805 (doi:10.1145/1102351.1102382)

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Publisher's URL: http://doi.acm.org/10.1145/1102351.1102382

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.

Item Type:Conference Proceedings
Additional Information:© ACM, 2005. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM International Conference Proceeding Series, Vol. 119 (2005) http://doi.acm.org/10.1145/1102351.1102382
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Girolami, Prof Mark
Authors: Girolami, M., and Rogers, S.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Publisher:ACM Press
ISBN:1595931805
Copyright Holders:Copyright © 2005 ACM Press
First Published:First published in ACM International Proceeding Series 119:241-248
Publisher Policy:Reproduced with the permission of the publisher

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