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Sensitivity and Generalization of SVM with Weighted and Reduced Features

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Reliable Knowledge Discovery

Abstract

Support Vector Machine, as a modern statistical learning method based on the principle of structure risk minimization rather than the empirical risk minimization, has been widely applied to the small-sample, non-linear and high-dimensional problems. Many new versions of SVM have been proposed to improve the performance SVM. Some of the new versions focus on processing the features of SVM. For example, give the features weight values or reduce some unnecessary features. A new feature weighted SVM and a feature reduced SVM are proposed in this chapter. The two versions of SVM are applied to the regression works to predict the price of a certain stock, and the outputs are compared with classical SVM. The results showed that the proposed feature weighted SVM can improve the accuracy of the regression, and the proposed featured reduced SVM is sensitive to the data sample for testing

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Author information

Authors and Affiliations

  1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Yan-xing Hu & James N. K. Liu

  2. Software Engineering School of Xi’an Jiaotong University, Xi’an, China

    Li-wei Jia

Authors
  1. Yan-xing Hu
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  2. James N. K. Liu
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  3. Li-wei Jia
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Corresponding author

Correspondence to Yan-xing Hu .

Editor information

Editors and Affiliations

  1. , School of Information Technology, Deakin University, 221 Burwood Highway, Burwood, 3125, Victoria, Australia

    Honghua Dai

  2. , Computing, Hong Kong Polytechnic University, Man Wai Building, Hunghom, PQ806, Hong Kong SAR

    James N. K. Liu

  3. , Department of Knowledge Engineering, Maastricht University, Maastricht, 6200MD, Netherlands

    Evgueni Smirnov

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Hu, Yx., Liu, J.N.K., Jia, Lw. (2012). Sensitivity and Generalization of SVM with Weighted and Reduced Features. In: Dai, H., Liu, J., Smirnov, E. (eds) Reliable Knowledge Discovery. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1903-7_9

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  • DOI: https://doi.org/10.1007/978-1-4614-1903-7_9

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4614-1902-0

  • Online ISBN: 978-1-4614-1903-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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