Adaptive margin slack minimization in RKHS for classification
Paper in proceedings, 2016

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.

Structural Risk Minimization

L2-SVM

Adaptive Margin

Reproducing Kernel Hilbert Space

Author

Yinan Yu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Konstantinos I. Diamantaras

TEI of Thessaloniki

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Princeton University

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

S. Y. Kung

TEI of Thessaloniki

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Princeton University

41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20-25 March 2016

1520-6149 (ISSN)

2319-2323

Subject Categories

Signal Processing

DOI

10.1109/ICASSP.2016.7472091

ISBN

978-1-4799-9988-0

More information

Latest update

7/23/2018