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Implementation of the MLP Kernel

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5507))

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Abstract

This paper presents a MLP kernel. It maps all patterns in a class into a single point in the output layer space and maps different classes into different points. These widely separated class points can be used for further classifications. It is a layered feed-forward network. Each layer is trained using the class differences and trained independently layer after layer using a bottom-up construction. The class labels are not used in the training process. It can be used in separating multiple classes.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Republic of China

    Cheng-Yuan Liou & Wei-Chen Cheng

Authors
  1. Cheng-Yuan Liou
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  2. Wei-Chen Cheng
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Editor information

Editors and Affiliations

  1. Network Design and Research Center, Kyushu Institute of Technology,, 680-4, Kawazu, Iizuka,, 820-8502, Fukuoka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street,, 1142, Auckland, New Zealand

    George Coghill

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Liou, CY., Cheng, WC. (2009). Implementation of the MLP Kernel. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_46

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  • DOI: https://doi.org/10.1007/978-3-642-03040-6_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03039-0

  • Online ISBN: 978-3-642-03040-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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