关键词: 支持向量机, 超平面, 多分类, 手写体汉字识别, 算法

Abstract: Based on the idea of nonparallel hyperplanes, a novel multi-class cluster support vector machine method was proposed to settle the multi-class classification problem of support vector machines. For a k-class classification problem, it trained k-hyperplanes respectively, and each one lay as close as possible to self-class while apart from the rest classes as far as possible. Then, labels of new samples were determined by the class of their nearest hyperplane belonging to, thus the inherent limitations of OAO and OAA methods can be avoided, such as “decision blind-area” and “unbalanced classes”. Finally, experiments on UCI and HCL2000 datasets showed that the proposed method outperformed traditional OAO and OAA etc. methods in terms of recognition accuracy significantly.

Key words: support vector machine, hyperplane, multi-class, handwritten Chinese character recognition, algorithm