Abstract
Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVMapproac h was originally developed for binary classification problems. In this paper SVMarc hitectures 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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Schwenker, F. (2001). Solving Multi-Class Pattern Recognition Problems with Tree-Structured Support Vector Machines. In: Radig, B., Florczyk, S. (eds) Pattern Recognition. DAGM 2001. Lecture Notes in Computer Science, vol 2191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45404-7_38
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DOI: https://doi.org/10.1007/3-540-45404-7_38
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