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Multiple Random Subset-Kernel Learning

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Computer Analysis of Images and Patterns (CAIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6854))

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Abstract

In this paper, the multiple random subset-kernel learning (MRSKL) algorithm is proposed. In MRSKL, a subset of training samples is randomly selected for each kernel with randomly set parameters, and the kernels with optimal weights are combined for classification. A linear support vector machine (SVM) is adopted to determine the optimal kernel weights; therefore, MRSKL is based on a hierarchical SVM. MRSKL outperforms a single SVM even when using a small number of samples (200 to 400 out of 20,000 training samples), while the SVM requires more than 4,000 support vectors.

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Author information

Authors and Affiliations

  1. Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8568, Japan

    Kenji Nishida & Jun Fujiki

  2. Faculty of Engineering, Hiroshima University, Kagamiyama 1-7-1, Higashi-Hiroshima Hiroshima, 739-8521, Japan

    Takio Kurita

Authors
  1. Kenji Nishida
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  2. Jun Fujiki
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  3. Takio Kurita
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Editor information

Editors and Affiliations

  1. Dpto. Matematica Aplicada I, Escuaela Técnica Superior de Ingeniería Informática, Universite de Sevilla, Avda. Reina Mercedes, s/n, 41012, Sevilla, Spain

    Pedro Real

  2. Departamento de Matemática Aplicada I, Escuela Técnica Superior de Ingeniería Informática, University of Seville, Avenida Reina Mercedes s/n, 41012, Sevilla, Spain

    Daniel Diaz-Pernil  & Helena Molina-Abril  & 

  3. Departamento de Didáctica de la Mathemática y de las CC.Experimentales, Universidad del País Vasco-Esukal Herriko Unibertsitatea, Escuela Universitaria de Magisterio, Ramón y Cajal, 72, 48014, Bilbao (Bizcaia), Spain

    Ainhoa Berciano

  4. Institute of Computer Graphics and Algorithms, Pattern Recognition and Image Processing Group, Vienna University of Technology, Favoritenstraße 9/186-3, 1040, Vienna, Austria

    Walter Kropatsch

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© 2011 Springer-Verlag Berlin Heidelberg

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Nishida, K., Fujiki, J., Kurita, T. (2011). Multiple Random Subset-Kernel Learning. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_42

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  • DOI: https://doi.org/10.1007/978-3-642-23672-3_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23671-6

  • Online ISBN: 978-3-642-23672-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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