Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
Identifieur interne : 001A67 ( Main/Exploration ); précédent : 001A66; suivant : 001A68Tree-Structured Support Vector Machines for Multi-class Pattern Recognition
Auteurs : Friedhelm Schwenker [Suisse, Allemagne] ; Günther Palm [Suisse, Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
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.
Url:
DOI: 10.1007/3-540-48219-9_41
Affiliations:
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