Gender Recognition Using a Min-Max Modular Support Vector Machine with Equal Clustering

  • Jun Luo
  • Bao-Liang Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Through task decomposition and module combination, min-max modular support vector machines (M3-SVMs) can be successfully used for different pattern classification tasks. Based on an equal clustering algorithm, M3-SVMs can divide the training data set of the original problem into several subsets with nearly equal number of samples, and combine them to a series of balanced subproblems which can be trained more efficiently and effectively. In this paper, we explore the use of M3-SVMs with equal clustering method in gender recognition. The experimental results show that M3-SVMs with equal clustering method can be successfully used for gender recognition and make the classification more efficient and accurate.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Luo
    • 1
  • Bao-Liang Lu
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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