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The Research on Newly Improved Bound Semi-supervised Support Vector Machine Learning Algorithm

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Information and Management Engineering (ICCIC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 236))

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Abstract

SVM is the structural risk minimization of statistical learning theory developed on the basis of a pattern recognition method, based on limited sample information and the complexity of the model to find the best compromise between the generalization ability. As there is a supervised learning method, the standard SVM classification requires supervised learning algorithm based on the principle: from a limited number of labeled samples to learn the rules and the rule extended to the unknown non-tag samples.

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Author information

Authors and Affiliations

  1. Huzhou Teachers College, Huzhou, Zhejiang, China

    Xue Deqian

Authors
  1. Xue Deqian
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Editor information

Editors and Affiliations

  1. Nanchang University, 235 Nanjing Donglu, 330047, Nanchang, China

    Min Zhu

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© 2011 Springer-Verlag Berlin Heidelberg

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Deqian, X. (2011). The Research on Newly Improved Bound Semi-supervised Support Vector Machine Learning Algorithm. In: Zhu, M. (eds) Information and Management Engineering. ICCIC 2011. Communications in Computer and Information Science, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24097-3_57

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  • DOI: https://doi.org/10.1007/978-3-642-24097-3_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24096-6

  • Online ISBN: 978-3-642-24097-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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