摘要： 单一技术无法有效解决多类分类问题。为此，提出一种基于一对多支持向量机(SVM)的基本概率分配输出方法，并与置信最大熵模型的D-S证据组合方法结合，给出基于SVM概率输出和证据理论的多分类模型。在3种UCI标准数据集上的仿真结果表明，该方法的分类精度优于传统的一对多和一对一硬输出方法，是一种有效的多类分类方法。

关键词: 证据理论, 支持向量机, 输出概率建模, 信息融合

Abstract: One-technology do not solve multi-class classification problem, on the basis of this, a basic probability output distribution method based on One-Against-All(OAA) Support Vector Machine(SVM) is proposed, a multi-class model based on Support Vector Machine(SVM) probability output and evidence theory is put forward by integrating one-against-all multi-class SVM with max-entropy D-S theory, . Simulations results on three datasets of UCI repository show that the method has higher classification precision than hard output method OAA and OAO.

Key words: evidence theory, Support Vector Machine(SVM), output probability modeling, information fusion

中图分类号: 

• TP18