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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)

Abstract

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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References

  1. Moghaddam, B., Yang, M.H.: Gender Classification with Support Vector Machines. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 306–311 (2000)Google Scholar
  2. Lu, B.L., Wang, K.A., Utiyama, M., Isahara, H.: A Part-versus-part Method for Massively Parallel Training of Support Vector Machines. In: Proceedings of IJCNN 2004, Budapast, pp. 735–740 (2004)Google Scholar
  3. Wang, K.A., Lu, B.L.: Task Decomposition Using Geometric Relation for Min-Max Modular SVM. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 887–892. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. Lian, H.C., Lu, B.L., Takikawa, E., Hosoi, S.: Gender Recognition Using A Min-max Modular Support Vector Machine. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 438–441. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. Wen, Y.M., Lu, B.L., Zhao, H.: Equal Clustering Makes Min-Max Modular Support Vector Machines More Efficient. In: ICONIP 2005, Taipei (2005)Google Scholar
  6. Lu, B.L., Ito, M.: Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification. IEEE Transcations on Neural Network 10(5), 1244–1256 (1999)CrossRefGoogle Scholar
  7. Choudhury, A., Nair, C.P., Keane, A.J.: A Data Parallel Approach for Large-Scale Gaussian Process Modeling. In: Proceedings of the Second SIAM International Conference on Data Mining (2002)Google Scholar
  8. Gao, W., Cao, B., Shan, S.G., Zhou, D.L., Zhang, X.H., Zhao, D.B.: The CAS-PEAL Large-Scale Chinese Face Database and Evaluation Protocols, Technical Report No. JDL_TR_04_FR_001, Joint Research & Development Laboratory, CAS (2004)Google Scholar

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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