Binary classification by minimizing the mean squared slack
File(s)
Accepted version
Author(s)
Kotti, Marg
Diamantaras, Konstantinos
Type
Conference Paper
Abstract
The paper presents a new binary classification method based on the minimization of the slack variables energy called the Mean Squared Slack (MSS). We deliver preliminary mathematical results which support the motivation behind our approach. We show that (a) in the linearly separable case the minimum MSS is attained at a separating vector, while (b) the minimizer in the linearly non-separable case is bounded but not zero. The method is conceptually simple: it solves a linear system at each iteration and it converges, typically, within a few iterations. Its complexity is obviously related to the size of the system which, in the linear case, is equal to the input pattern dimension. The method is extended to the non-linear case using kernels. Simulations demonstrate that the method is competitive with respect to computation time, accuracy, and generalization performance compared to state of the art SVM methods. © 2012 IEEE.
Date Issued
2012-03
Citation
IEEE Int. Conf. Acoustics, Speech, and Signal Processing, 2012, pp.2057-2060
ISBN
978-1-4673-0045-2
ISSN
1520-6149
Publisher
IEEE
Start Page
2057
End Page
2060
Journal / Book Title
IEEE Int. Conf. Acoustics, Speech, and Signal Processing
Copyright Statement
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Description
05.08.13 KB. Ok to add accepted version to Spira. IEEE policy
Source
ICASSP 2012
Source Place
Kyoto, Japan
Start Date
2012-03-25
Finish Date
2012-03-30
Coverage Spatial
Kyoto, Japan