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Adaptive margin slack minimization in RKHS for classification

Yinan Yu (Institutionen för signaler och system, Signalbehandling) ; Konstantinos I. Diamantaras ; Tomas McKelvey (Institutionen för signaler och system, Signalbehandling) ; S. Y. Kung
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016; Shanghai International Convention CenterShanghai; China; 20 March 2016 through 25 March 2016 (1520-6149). p. 2319-2323. (2016)
[Konferensbidrag, refereegranskat]

In this paper, we design a novel regularized empirical risk minimization technique for classification called Adaptive Margin Slack Minimization (AMSM). The proposed method is based on minimizing a regularized upper bound of the misclassification error. Compared to the cost function of the classical L2-SVM, AMSM can be interpreted as minimizing a tighter bound with some additional flexibilities regarding the choice of marginal hyperplane. A hyperparameter-free adaptive algorithm is presented for finding a solution to the proposed risk function. Numerical results shows that AMSM outperforms L2-SVM on the tested standard datasets.

Nyckelord: Adaptive Margin; L2-SVM; Reproducing Kernel Hilbert Space; Structural Risk Minimization



Denna post skapades 2016-07-12.
CPL Pubid: 239280

 

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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling

Ämnesområden

Signalbehandling

Chalmers infrastruktur