Integrating the Evidence Framework and the Support Vector Machine

James Tin-Yau Kwok

Integrating the Evidence Framework and the Support Vector Machine

James Tin-Yau Kwok

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

Abstract

In this pap er, we show that training of the support vector machine (SVM) can b e interpreted as performing the level 1 inference of MacKay's evidence framework. We further on show that levels 2 and 3 can also b e applied to SVM. This allows automatic adjustment of the regularization parameter and the kernel parameter. More importantly, it op ens up a wealth of Bayesian tools for use with SVM. Performance is evaluated on both synthetic and real-world data sets.

Original language	English
Title of host publication	ESANN'1999 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 21-23 April 1999, D-Facto public.
Publisher	ESANN
Pages	177-182
Number of pages	6
ISBN (Print)	26000499X
Publication status	Published - 21 Apr 1999
Externally published	Yes

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Cite this

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title = "Integrating the Evidence Framework and the Support Vector Machine",

abstract = "In this pap er, we show that training of the support vector machine (SVM) can b e interpreted as performing the level 1 inference of MacKay's evidence framework. We further on show that levels 2 and 3 can also b e applied to SVM. This allows automatic adjustment of the regularization parameter and the kernel parameter. More importantly, it op ens up a wealth of Bayesian tools for use with SVM. Performance is evaluated on both synthetic and real-world data sets.",

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AB - In this pap er, we show that training of the support vector machine (SVM) can b e interpreted as performing the level 1 inference of MacKay's evidence framework. We further on show that levels 2 and 3 can also b e applied to SVM. This allows automatic adjustment of the regularization parameter and the kernel parameter. More importantly, it op ens up a wealth of Bayesian tools for use with SVM. Performance is evaluated on both synthetic and real-world data sets.

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M3 - Conference contribution

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BT - ESANN'1999 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 21-23 April 1999, D-Facto public.

PB - ESANN

ER -

Integrating the Evidence Framework and the Support Vector Machine

Abstract

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