Please use this identifier to cite or link to this item: https://doi.org/10.1162/089976603322297368
Title: Bayesian trigonometric support vector classifier
Authors: Chu, W.
Keerthi, S.S. 
Ong, C.J. 
Issue Date: Sep-2003
Source: Chu, W., Keerthi, S.S., Ong, C.J. (2003-09). Bayesian trigonometric support vector classifier. Neural Computation 15 (9) : 2227-2254. ScholarBank@NUS Repository. https://doi.org/10.1162/089976603322297368
Abstract: This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/59619
ISSN: 08997667
DOI: 10.1162/089976603322297368
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

20
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

16
checked on Nov 20, 2017

Page view(s)

29
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.