文献类型：期刊文献

英文题名：Three-fold structured classifier design based on matrix pattern

作者：Wang, Zhe[1];Zhu, Changming[1];Gao, Daqi[1];Chen, Songcan[2]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Nanjing Univ Aeronaut & Astronaut, Dept Comp Sci & Engn, Nanjing 210016, Jiangsu, Peoples R China

年份：2013

卷号：46

期号：6

起止页码：1532

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20130615996154);WOS:【SCI-EXPANDED(收录号:WOS:000315369900002)】；

基金：The authors would like to thank Natural Science Foundations of China under Grant nos. 61272198, 60903091, 21176077, and 61170151, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant nos. 20090074120003, and the Fundamental Research Funds for the Central Universities for support.

语种：英文

外文关键词：Vector pattern; Matrix pattern; Global structure; Local structure; Classifier design; Pattern recognition

摘要：The traditional vectorized classifier is supposed to incorporate the class structural information but ignore the individual structure of single pattern. In contrast, the matrixized classifier is supposed to consider both the class and the individual structures, and thus gets a superior performance to the vectorized classifier. In this paper, we explore one middle granularity named the cluster between the class and individual, and introduce the cluster structure that means the structure within each class into the matrixized classifier design. Doing so can simultaneously utilize the class, the cluster, and the individual structures in the way that is from global to point. Therefore, the proposed classifier design here owns the three-fold structural information, and can bring the classification performance to an improving trend. In practice, we adopt the Modification of Ho-Kashyap algorithm with Squared approximation of the misclassification errors (MHKS) as the learning paradigm and develop a Three-fold Structured MHKS named TSMHKS. The advantage of the three-fold structural learning framework is considering different close degrees between samples so as to improve the performance. The experimental results demonstrate the feasibility and effectiveness of the TSMHKS. Furthermore, we discuss the theoretical and experimental generalization bound of the proposed algorithm. (c) 2012 Elsevier Ltd. All rights reserved.