Simplifying SVM with weighted LVQ algorithm

Authors:
Marcin Blachnik;Mirosław Kordos
Affiliations:
Silesian University of Technology, Department of Management and Informatics, Katowice, Krasinskiego, Poland;University of Bielsko-Biala, Department of Mathematics and Informatics, Bielsko-Biała, Willowa, Poland
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

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Abstract

Reduced Set SVMs (RS-SVM) are a group of methods that simplify the internal structure of SVM models, while keeping the SVMs' decision boundaries as similar as possible to the original ones. RS-SVMs are very useful in reducing computational complexity of the original models. They accelerate the decision process by reducing the number of support vectors. They are especially important for large datasets, when lots of support vectors are selected. They also can be very useful for understanding the internal structure of SVM models by the use of prototype-based rules. This paper presents a new method based on the modified version of the LVQ algorithm called WLVQ, which combines both of the objectives: computational complexity reduction and generation of prototypebased rules.