In this paper, a non-balanced binary tree is proposed for extending support vector machines (SVM) to multi-class problems. The non-balanced binary tree is constructed based on the prior distribution of samples, which can make the more separable classes separated at the upper node of the binary tree. For an k class problem, this method only needs k-1 SVM classifiers in the training phase, while it has less than k binary test when making a decision. Further, this method can avoid the unclassifiable regions that exist in the conventional SVMs. The experimental result indicates that maintaining comparable accuracy, this method is faster than other methods in classification. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Siyu, X., Jiuxian, L., Liangzheng, X., & Chunhua, J. (2007). Tree-structured support vector machines for multi-class classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 392–398). https://doi.org/10.1007/978-3-540-72395-0_50
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