Midpoint-Validation Method for Support Vector Machine Classification

Authors:
Hiroki Tamura;Koichi Tanno
Affiliations:
-;-
Venue:
IEICE - Transactions on Information and Systems
Year:
2008

Citing 8
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Abstract

In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method.