Adaptive margin slack minimization in RKHS for classification

Yinan Yu; Konstantinos I. Diamantaras; Tomas McKelvey; Sun-Yuan Kung

ICASSP 2016accepted0 citations

Adaptive margin slack minimization in RKHS for classification

Yinan Yu, Konstantinos I. Diamantaras, Tomas McKelvey, Sun-Yuan Kung

Abstract

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.

BibTeX

@inproceedings{icassp2016_adaptivemarginsl,
  title = {Adaptive margin slack minimization in RKHS for classification},
  author = {Yinan Yu and Konstantinos I. Diamantaras and Tomas McKelvey and Sun-Yuan Kung},
  booktitle = {ICASSP 2016},
  year = {2016}
}