Support vector classifier with hyperbolic tangent penalty function

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...

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Bibliographic Details
Main Authors: Perez-Cruz, F., Navia-Vazquez, A., Alarcon-Diana, P.L., Artes-Rodriguez, A.
Format: Conference Proceeding
Language:English
Subjects:
Lagrangian functions
Machine learning
Polynomials
Quadratic programming
Risk management
Static VAr compensators
Support vector machine classification
Support vector machines
Training data
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Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2000.860145