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Universal Learning Machines

  • Conference paper
Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

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Abstract

All existing learning methods have particular bias that makes them suitable for specific kind of problems. Universal Learning Machine (ULM) should find the simplest data model for arbitrary data distributions. Several ways to create ULMs are outlined, and an algorithm based on creation of new global and local features combined with meta-learning is introduced. This algorithm is able to find simple solutions that sophisticated algorithms ignore, learn complex Boolean functions, complicated probability distributions, as well as the problems requiring multiresolution decision borders.

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Author information

Authors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Google: W Duch, Toruń, Poland

    Włodzisław Duch & Tomasz Maszczyk

Authors
  1. Włodzisław Duch
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  2. Tomasz Maszczyk
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Duch, W., Maszczyk, T. (2009). Universal Learning Machines. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_23

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  • DOI: https://doi.org/10.1007/978-3-642-10684-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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