A Bayesian multiple kernel learning framework for single and multiple output regression

Mehmet Gönen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on classification formulations and there are few attempts for regression. We propose a fully conjugate Bayesian formulation and derive a deterministic variational approximation for single output regression. We then show that the proposed formulation can be extended to multiple output regression. We illustrate the effectiveness of our approach on a single output benchmark data set. Our framework outperforms previously reported results with better generalization performance on two image recognition data sets using both single and multiple output formulations.

Original languageEnglish (US)
Title of host publicationECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration
PublisherIOS Press
Pages354-359
Number of pages6
ISBN (Print)9781614990970
DOIs
StatePublished - 2012
Event20th European Conference on Artificial Intelligence, ECAI 2012 - Montpellier, France
Duration: Aug 27 2012Aug 31 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume242
ISSN (Print)0922-6389

Conference

Conference20th European Conference on Artificial Intelligence, ECAI 2012
CountryFrance
CityMontpellier
Period8/27/128/31/12

ASJC Scopus subject areas

  • Artificial Intelligence

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    Gönen, M. (2012). A Bayesian multiple kernel learning framework for single and multiple output regression. In ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration (pp. 354-359). (Frontiers in Artificial Intelligence and Applications; Vol. 242). IOS Press. https://doi.org/10.3233/978-1-61499-098-7-354