Adapting Kernels by Variational Approach in SVM

Junbin Gao; Steve Gunn; Jaz Kandola

doi:10.1007/3-540-36187-1_35

Adapting Kernels by Variational Approach in SVM

Junbin Gao
, Steve Gunn
, Jaz Kandola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

This paper proposed a variational Bayesian approach for the SVM regression based on the likelihood model of an infinite mixture of Gaussians. To evaluate this approach the method was applied to synthetic datasets. We compared this new approximation approach with the standard SVM algorithm as well as other well established methods such as Gaussian Process.

Original language	English
Title of host publication	AI 2002: Advances in Artificial Intelligence
Editors	Bob McKay, John Slaney
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	395-406
ISBN (Print)	9783540001973, 3540001972
DOIs	https://doi.org/10.1007/3-540-36187-1_35
Publication status	Published - 2002
Event	15th Australian Joint Conference on Artificial Intelligence - Canberra, Australia Duration: 2 Dec 2002 → 6 Dec 2002

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Number	2557
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th Australian Joint Conference on Artificial Intelligence
City	Canberra, Australia
Period	2/12/02 → 6/12/02

Keywords

Artificial Intelligence and Image Processing

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10.1007/3-540-36187-1_35

https://hdl.handle.net/1959.11/4533

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@inproceedings{de781b1047ca4d168c897034605e766b,

title = "Adapting Kernels by Variational Approach in SVM",

abstract = "This paper proposed a variational Bayesian approach for the SVM regression based on the likelihood model of an infinite mixture of Gaussians. To evaluate this approach the method was applied to synthetic datasets. We compared this new approximation approach with the standard SVM algorithm as well as other well established methods such as Gaussian Process.",

keywords = "Artificial Intelligence and Image Processing",

author = "Junbin Gao and Steve Gunn and Jaz Kandola",

year = "2002",

doi = "10.1007/3-540-36187-1\_35",

language = "English",

isbn = "9783540001973",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

number = "2557",

pages = "395--406",

editor = "Bob McKay and John Slaney",

booktitle = "AI 2002: Advances in Artificial Intelligence",

note = "15th Australian Joint Conference on Artificial Intelligence ; Conference date: 02-12-2002 Through 06-12-2002",

}

Gao, J, Gunn, S & Kandola, J 2002, Adapting Kernels by Variational Approach in SVM. in B McKay & J Slaney (eds), AI 2002: Advances in Artificial Intelligence. Lecture Notes in Computer Science, no. 2557, Springer, Berlin, Germany, pp. 395-406, 15th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, 2/12/02. https://doi.org/10.1007/3-540-36187-1_35

TY - GEN

T1 - Adapting Kernels by Variational Approach in SVM

AU - Gao, Junbin

AU - Gunn, Steve

AU - Kandola, Jaz

PY - 2002

Y1 - 2002

N2 - This paper proposed a variational Bayesian approach for the SVM regression based on the likelihood model of an infinite mixture of Gaussians. To evaluate this approach the method was applied to synthetic datasets. We compared this new approximation approach with the standard SVM algorithm as well as other well established methods such as Gaussian Process.

AB - This paper proposed a variational Bayesian approach for the SVM regression based on the likelihood model of an infinite mixture of Gaussians. To evaluate this approach the method was applied to synthetic datasets. We compared this new approximation approach with the standard SVM algorithm as well as other well established methods such as Gaussian Process.

KW - Artificial Intelligence and Image Processing

UR - http://trove.nla.gov.au/work/17085154

U2 - 10.1007/3-540-36187-1_35

DO - 10.1007/3-540-36187-1_35

M3 - Conference contribution

SN - 9783540001973

SN - 3540001972

T3 - Lecture Notes in Computer Science

SP - 395

EP - 406

BT - AI 2002: Advances in Artificial Intelligence

A2 - McKay, Bob

A2 - Slaney, John

PB - Springer

CY - Berlin, Germany

T2 - 15th Australian Joint Conference on Artificial Intelligence

Y2 - 2 December 2002 through 6 December 2002

ER -

Adapting Kernels by Variational Approach in SVM

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