An Improved Binary Tree SVM Classification Algorithm Based on Bayesian

摘要

Due to the great generalization and the support of statistics, support vector machines (SVMs) has been widely applied to resolving multi-class classification problem. Numbers of multi-class SVM have been proposed. Compared with other multi-class SVM, binary tree of SVM (BTS) takes a good advantage of lower time consuming. However, there is some unnecessary data reassignment during constructing a binary tree, which makes BTS can't resolve the high-dimensional multi-class classification problem accurately. In this paper, a bayesian-based BTS classification algorithm (b-BTS) has been proposed. Experiments demonstrated that b-BTS is superior to BTS in resolving classification problem, such as image classification problem.