Abstract: Support Vector Machines（SVMs） is originally designed for binary classification.How to extend it for multi-category classification is one of hot research issues.This paper emphasizes on analyzing decomposing and reconstitution methodology of SVM multi-classification algorithms，and discusses two key factors which affect performances of categories in detail：decomposing strategy and composing strategy.The paper does experiments to validate authors’ opinions and then comparesall kinds of SVM multi-category classification，including M-ary SVMs and fuzzy SVMs.

摘要： 支持向量机最初是针对两类分类问题提出的，如何将其推广至多类分类问题是当前SVM研究中的热点问题之一。主要针对支持向量机多类分类方法中的分解重构法进行了深入分析，详细讨论了影响分类器性能的两个关键因素：分解策略和组合策略，并通过实验验证了该观点。最后，通过实验对比了包括M-ary 支持向量机和模糊支持向量机的SVM多类分类方法。