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Learning with Sequential Minimal Transductive Support Vector Machine

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Book cover Frontiers in Algorithmics (FAW 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5598))

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Abstract

While transductive support vector machine (TSVM) utilizes the information carried by the unlabeled samples for classification and acquires better classification performance than support vector machine (SVM), the number of positive samples must be appointed before training and it is not changed during the training phase. In this paper, a sequential minimal transductive support vector machine (SMTSVM) is discussed to overcome the deficiency in TSVM. It solves the problem of estimation the penalty value after changing a temporary label by introducing the sequential minimal way. The experimental results show that SMTSVM is very promising.

Supported by the Shanghai Leading Academic Discipline Project (No. S30405), and the NSF of Shanghai Normal University (No. SK200937).

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Author information

Authors and Affiliations

  1. Department of Mathematics, Shanghai Normal University, 200234, P.R. China

    Xinjun Peng

  2. Scientific Computing Key Laboratory of Shanghai Universities, 200234, P.R. China

    Xinjun Peng

  3. Department of Mathematics, Shanghai University, 200444, P.R. China

    Yifei Wang

Authors
  1. Xinjun Peng
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  2. Yifei Wang
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Editor information

Editors and Affiliations

  1. Department of Computer Science,, City University of Hong Kong, No. 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China

    Xiaotie Deng

  2. Computer Science Department, Cornell University, 5144 Upson Hall, NY 14853, Ithaca, USA

    John E. Hopcroft

  3. Provincial Key Laboratory of High-Performance Computing, Jiangxi Normal University, 330027, Nanchang, China

    Jinyun Xue

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© 2009 Springer-Verlag Berlin Heidelberg

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Peng, X., Wang, Y. (2009). Learning with Sequential Minimal Transductive Support Vector Machine. In: Deng, X., Hopcroft, J.E., Xue, J. (eds) Frontiers in Algorithmics. FAW 2009. Lecture Notes in Computer Science, vol 5598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02270-8_23

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  • DOI: https://doi.org/10.1007/978-3-642-02270-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02269-2

  • Online ISBN: 978-3-642-02270-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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