Tree-Structured Support Vector Machines for Multi-class Pattern Recognition

Authors:
Friedhelm Schwenker;Günther Palm
Affiliations:
-;-
Venue:
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Year:
2001

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Abstract

Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class pattern recognition problem. Numerical results for different classifiers on a benchmark data set handwritten digits are presented.