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Using Double-Layer One-Class Classification for Anti-jamming Information Filtering

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3498))

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Abstract

One-class classification, a derivative of newly developed Support Vector Machine (SVM), obtains a spherically shaped boundary around a dataset, and the boundary can be made flexible by using kernel methods. In this paper, a new method is presented to improve the speed and accuracy of one-class classification. This method can be applied to anti-jamming information filtering with the aim of making it more practical. The experimental results show that the algorithm has better performance in general.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, 200030, China

    Qiang Sun, Jianhua Li & Shenghong Li

  2. ECE Department, UCSD, 9500 Gilman Drive, MC 0407, La Jolla, CA, 92093-0407, USA

    Xinran Liang

Authors
  1. Qiang Sun
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  2. Jianhua Li
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  3. Xinran Liang
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  4. Shenghong Li
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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

    Xiao-Feng 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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Sun, Q., Li, J., Liang, X., Li, S. (2005). Using Double-Layer One-Class Classification for Anti-jamming Information Filtering. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_58

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  • DOI: https://doi.org/10.1007/11427469_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25914-5

  • Online ISBN: 978-3-540-32069-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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