文献类型：期刊文献

英文题名：Support vector machine classifiers using RBF kernels with clustering-based centers and widths

作者：Gao, Daqi[1]; Zhang, Tao[1]

机构：[1] Department of Computer Science, East China University of Science and Technology, Shanghai 200237, China

年份：2007

起止页码：2971

外文期刊名：IEEE International Conference on Neural Networks - Conference Proceedings

收录：EI(收录号：20083811574762)

语种：英文

外文关键词：Cluster analysis - Lagrange multipliers - Character recognition - Radial basis function networks

摘要：This paper focuses on support vector machines (SVMs) with radial basis function (RBF) kernels to solve the large-scale classification problems. We decompose a large-scale learning problem into multiple two-class problems with the one-verse-all decomposition technique, and then propose an adaptively clustering method. An initial support vector (SV) coincides with a certain clustering center, and its width is equal to the max Euclid distance in the clustering region. Therefore, the initial number of SVs is equal to that of the clustering centers, and different RBF kernels are with different widths. The optimization of SVMs is only to determine the Lagrange multipliers. The resulting kernel space for optimization becomes relatively lower in dimensionality, and the final SVs are from a part of the clustering centers. The experimental results for the letter and the handwritten digit recognitions show that the proposed methods are effective. ?2007 IEEE.