Please use this identifier to cite or link to this item:
|Title:||Bayesian trigonometric support vector classifier||Authors:||Chu, W.
|Issue Date:||Sep-2003||Citation:||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.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.