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Hierarchical support vector machines for multi-class pattern recognition
Schwenker, F.;
Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
Volume 2,
30 Aug.-1 Sept. 2000
Page(s):561
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565 vol.2
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 problem. Numerical results for different classifiers on a benchmark data set of handwritten digits are presented
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