Hierarchical support vector machines for multi-class pattern recognition

被引：0
|
作者：
Schwenker, Friedhelm [1 ]
机构：
[1] Univ of Ulm, Ulm, Germany
来源：
International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES | 2000年 / 2卷
关键词：
Learning algorithms - Learning systems - Statistical methods - Trees (mathematics);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：561 / 565
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