Please use this identifier to cite or link to this item: https://doi.org/10.1162/089976603322297368
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dc.titleBayesian trigonometric support vector classifier
dc.contributor.authorChu, W.
dc.contributor.authorKeerthi, S.S.
dc.contributor.authorOng, C.J.
dc.date.accessioned2014-06-17T06:13:35Z
dc.date.available2014-06-17T06:13:35Z
dc.date.issued2003-09
dc.identifier.citationChu, 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
dc.identifier.issn08997667
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/59619
dc.description.abstractThis 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1162/089976603322297368
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1162/089976603322297368
dc.description.sourcetitleNeural Computation
dc.description.volume15
dc.description.issue9
dc.description.page2227-2254
dc.identifier.isiut000184319200011
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