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Multiple Kernel Learning with One-Level Optimization of Radius and Margin

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AI 2017: Advances in Artificial Intelligence (AI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10400))

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Abstract

Generalization error rates of support vector machines are closely related to the ratio of radius of sphere which includes all the data and the margin between the separating hyperplane and the data. There are already several attempts to formulate the multiple kernel learning of SVMs using the ratio rather than only the margin. Our approach is to combine the well known formulations of SVMs and SVDDs. The proposed model is a closed system and always reaches the global optimal solutions.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand

    Shinichi Yamada & Kourosh Neshatian

Authors
  1. Shinichi Yamada
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  2. Kourosh Neshatian
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Corresponding author

Correspondence to Shinichi Yamada .

Editor information

Editors and Affiliations

  1. La Trobe University, Melbourne, Australia

    Wei Peng

  2. La Trobe Business School, La Trobe University, Bundoora, Victoria, Australia

    Damminda Alahakoon

  3. RMIT University, Melbourne, Australia

    Xiaodong Li

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Yamada, S., Neshatian, K. (2017). Multiple Kernel Learning with One-Level Optimization of Radius and Margin. In: Peng, W., Alahakoon, D., Li, X. (eds) AI 2017: Advances in Artificial Intelligence. AI 2017. Lecture Notes in Computer Science(), vol 10400. Springer, Cham. https://doi.org/10.1007/978-3-319-63004-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-63004-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63003-8

  • Online ISBN: 978-3-319-63004-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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