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Journal of Zhejiang University SCIENCE A 2002 Vol.3 No.2 P.131-134

http://doi.org/10.1631/jzus.2002.0131


Probability output of multi-class support vector machines


Author(s):  XIN Dong, WU Zhao-hui, PAN Yun-he

Affiliation(s):  Department of Computer Science & Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   xindong@cs.zju.edu.cn

Key Words:  Support vector machines (SVM), Posterior probability, Multi-class, Speaker verification


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XIN Dong, WU Zhao-hui, PAN Yun-he. Probability output of multi-class support vector machines[J]. Journal of Zhejiang University Science A, 2002, 3(2): 131-134.

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author="XIN Dong, WU Zhao-hui, PAN Yun-he",
journal="Journal of Zhejiang University Science A",
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Abstract: 
A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[3] Compbell, W. M., Assaleh, K. T., 1999. Polynomial Classifier Techniques for Speaker Verification, Proc. ICASSP: p.321-324.

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[5] Kwok, J.T.Y., 1999, Moderating the outputs of support vector machine classifiers. Neural Networks, IEEE Transactions on, 5:1018-1031.

[6] Madevska-Bogdanova, A., Nikolic, D., 2000, A new approach of modifying SVM outputs, Proceedings of the IEEE-INNS-ENNS International Joint Conference. 6:395-398.

[7] Platt, J., 1999, Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, In: Advances in Large Margin Classifiers, MIT Press.

[8] Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A.J., Williamson, R.C., 1999. Estimating the support of a high-dimensional distribution, Microsoft Research Corporation Technical Report: MSR-TR-99-87.

[9] Vapnik, V. N., 1999. An overview of statistical learning theory. Neural Networks, IEEE Transactions, 5:988-999.

[10] Wan. V., Campbell, W. M., 2000. Support Vector Machines for Speaker Verification and Identification, Neural Networks for Signal Processing X. p.775-784

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