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Fuzzy Multi-class SVM Classifier Based on Optimal Directed Acyclic Graph Using in Similar Handwritten Chinese Characters Recognition

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

This paper proposes a method to improve generalization performance of multi-class support vector machines (SVM) based on directed acyclic graph (DAG). At first the structure of DAG is optimized according to training data and Jaakkola-Haussler bound, and then we define fuzzy membership function for each class which is obtained by using average operator in the testing stage and the final recognition result is the class with maximum membership. As a result of our experiment for similar handwritten Chinese characters recognition, the generalization ability of the novel fuzzy multi-class DAG-based SVM classifier is better than that of pair-wise SVM classifier with other combination strategies and its execution time is almost the same as the original DAG.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Shijiazhuang Railway Institute, Shijiazhuang, 050043, China

    Jun Feng & Jinsheng Fan

  2. Information Engineering School, University of Science and Technology Beijing, Beijing, 100083, China

    Jun Feng & Yang Yang

Authors
  1. Jun Feng
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  2. Yang Yang
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  3. Jinsheng Fan
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Feng, J., Yang, Y., Fan, J. (2005). Fuzzy Multi-class SVM Classifier Based on Optimal Directed Acyclic Graph Using in Similar Handwritten Chinese Characters Recognition. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_140

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  • DOI: https://doi.org/10.1007/11427391_140

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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