Support vector classifier with hyperbolic tangent penalty function

Author

Pérez-Cruz, F. ; Navia-Vazquez, A. ; Alarcón-Diana, P.L. ; Artés-Rodríguez, A.

Author_Institution

Escuela Politecnica, Alcala Univ., Madrid, Spain

Volume

6

fYear

2000

fDate

2000

Firstpage

3458

Abstract

The support vector classifier is a new tool to solve classification problems, giving the classification boundary as a linear combination of the training samples. In non-separable problems with highly overlapped classes, the achieved classifiers are oversized. In this paper, we proposed to change the support vector classifier penalty function by an hyperbolic tangent one, obtaining as a result of the training phase a reduced support vector classifier with the same performance as the original one

Keywords

iterative methods; least squares approximations; pattern classification; quadratic programming; classification boundary; classification problems; highly overlapped classes; hyperbolic tangent; hyperbolic tangent penalty function; nonseparable problems; performance; raining phase; reduced support vector classifier; support vector classifier; training samples; Lagrangian functions; Machine learning; Polynomials; Quadratic programming; Risk management; Static VAr compensators; Support vector machine classification; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

Conference_Location

Istanbul

ISSN

1520-6149

Print_ISBN

0-7803-6293-4

Type

conf

DOI

10.1109/ICASSP.2000.860145

Filename

860145