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

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 publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages354-359
Number of pages6
Volume242
ISBN (Print)9781614990970
DOIs
Publication statusPublished - 2012
Externally publishedYes

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume242
ISSN (Print)09226389

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Gonen, M. (2012). A Bayesian multiple kernel learning framework for single and multiple output regression. In Frontiers in Artificial Intelligence and Applications (Vol. 242, pp. 354-359). (Frontiers in Artificial Intelligence and Applications; Vol. 242). IOS Press. https://doi.org/10.3233/978-1-61499-098-7-354