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Learning with Ensemble of Linear Perceptrons

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

In this paper we introduce a model of ensemble of linear perceptrons. The objective of the ensemble is to automatically divide the feature space into several regions and assign one ensemble member into each region and training the member to develop an expertise within the region. Utilizing the proposed ensemble model, the learning difficulty of each member can be reduced, thus achieving faster learning while guaranteeing the overall performance.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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Author information

Authors and Affiliations

  1. Future University-Hakodate, Kamedanakanocho 116-2, Hakodate City, Japan

    Pitoyo Hartono

  2. Waseda University, Ohkubo3-4-1 Shinjuku-ku, Tokyo, 169-8555, Japan

    Shuji Hashimoto

Authors
  1. Pitoyo Hartono
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  2. Shuji Hashimoto
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Hartono, P., Hashimoto, S. (2005). Learning with Ensemble of Linear Perceptrons. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_19

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  • DOI: https://doi.org/10.1007/11550907_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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