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Bayesian Trigonometric Support Vector Classifier.

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Title: Bayesian Trigonometric Support Vector Classifier.
Authors: Wei Chu engp935@nus.edu.sg, S. Sathiya Keerthi mpessk@nus.edu.sg, Chong Jin Ong1 mpeongcj@nus.edu.sg
Source: Neural Computation. Sep2003, Vol. 15 Issue 9, p2227. 28p. 5 Charts, 5 Graphs.
Subjects: Vector processing (Computer science), Bayesian analysis
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. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Wei+Chu%22">Wei Chu</searchLink><i> engp935@nus.edu.sg</i><br /><searchLink fieldCode="AR" term="%22S%2E+Sathiya+Keerthi%22">S. Sathiya Keerthi</searchLink><i> mpessk@nus.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Chong+Jin+Ong%22">Chong Jin Ong</searchLink><relatesTo>1</relatesTo><i> mpeongcj@nus.edu.sg</i>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1162/089976603322297368
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      – Code: eng
        Text: English
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      – SubjectFull: Bayesian analysis
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      – TitleFull: Bayesian Trigonometric Support Vector Classifier.
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            NameFull: Wei Chu
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            NameFull: S. Sathiya Keerthi
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            NameFull: Chong Jin Ong
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            – D: 01
              M: 09
              Text: Sep2003
              Type: published
              Y: 2003
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