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  3. Statistical Mechanics of Learning
  • Cited by 298
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    • Publisher:
      Cambridge University Press
      Publication date:
      June 2012
      March 2001
      ISBN:
      9781139164542
      9780521773072
      9780521774796
      DOI:
      https://doi.org/10.1017/CBO9781139164542
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.75kg, 342 Pages
      Dimensions:
      (247 x 174 mm)
      Weight & Pages:
      0.55kg, 344 Pages
      • Subjects:
      Computer Science, Pattern Recognition and Machine Learning, Computational Biology and Bioinformatics
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    • Selected: Digital
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    Subjects:
    Computer Science, Pattern Recognition and Machine Learning, Computational Biology and Bioinformatics
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    Book description

    Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, artificial neural networks, in which learning from examples may be described and understood. The contribution to this subject made over the last decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.

    Reviews

    ‘… recommended both to students of the subjects artificial intelligence, statistics, of interdisciplinary subjects in psychology and philosophy, and to scientists and applied researchers interested in concepts of intelligent learning processes.’

    Source: Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts

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