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 hand written digits are presented.
CITATION STYLE
Schwenker, F. (2001). Solving multi-class pattern recognition problems with tree-structured support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2191, pp. 283–290). Springer Verlag. https://doi.org/10.1007/3-540-45404-7_38
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