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Multi-Class SVM Classifier Based on Pairwise Coupling

  • Zeyu Li
  • Shiwei Tang
  • Shuicheng Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2388)

Abstract

In this paper, a novel structure is proposed to extend standard support vector classifier to multi-class cases. For a K-class classification task, an array of K optimal pairwise coupling classifier (O-PWC) is constructed, each of which is the most reliable and optimal for the corresponding class in the sense of cross entropy or square error. The final decision will be produced through combining the results of these K O-PWCs. The accuracy rate is improved while the computational cost will not increase too much. Our approach is applied to two applications: handwritten digital recognition on MNIST database and face recognition on Cambridge ORL face database, experimental results reveal that our method is effective and efficient.

Keywords

Support Vector Machine Face Recognition Face Image Probability Vector Probabilistic Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Zeyu Li
    • 1
  • Shiwei Tang
    • 1
  • Shuicheng Yan
    • 2
  1. 1.National Laboratory on Machine Perception and Center for Information SciencePeking UniversityBeijingP.R. China
  2. 2.Dept. of Info. science, School of Math. SciencesPeking UniversityBeijing

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