Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/4533
Title: | Adapting Kernels by Variational Approach in SVM | Contributor(s): | Gao, Junbin (author); Gunn, Steve (author); Kandola, Jaz (author) | Publication Date: | 2002 | DOI: | 10.1007/3-540-36187-1_35 | Handle Link: | https://hdl.handle.net/1959.11/4533 | 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. | Publication Type: | Conference Publication | Conference Details: | 15th Australian Joint Conference on Artificial Intelligence, Canberra, Australia, 2nd - 6th December, 2002 | Source of Publication: | AI 2002: Advances in Artificial Intelligence, p. 395-406 | Publisher: | Springer | Place of Publication: | Berlin, Germany | Fields of Research (FoR) 2008: | 080199 Artificial Intelligence and Image Processing not elsewhere classified | Socio-Economic Objective (SEO) 2008: | 890205 Information Processing Services (incl. Data Entry and Capture) | Peer Reviewed: | Yes | HERDC Category Description: | E1 Refereed Scholarly Conference Publication | Publisher/associated links: | http://trove.nla.gov.au/work/17085154 | Series Name: | Lecture Notes in Computer Science | Series Number : | 2557 |
---|---|
Appears in Collections: | Conference Publication |
Files in This Item:
File | Description | Size | Format |
---|
SCOPUSTM
Citations
7
checked on Mar 23, 2024
Page view(s)
1,218
checked on Mar 24, 2024
Download(s)
4
checked on Mar 24, 2024
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.