Statistical Mechanics of Learning

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A. Engel (Otto-von-Guericke-Universität Magdeburg, Germany) C. Van den Broeck (Limburgs Universitair Centrum, Belgium)

Statistical Mechanics of Learning

A. Engel (Otto-von-Guericke-Universität Magdeburg, Germany), C. Van den Broeck (Limburgs Universitair Centrum, Belgium)

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English

Cambridge University Press
09 May 2001

Mathematical theory of computation; Artificial intelligence; Machine learning

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.

By: A. Engel (Otto-von-Guericke-Universität Magdeburg Germany), C. Van den Broeck (Limburgs Universitair Centrum, Belgium)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 244mm, Width: 170mm, Spine: 21mm
Weight: 750g
ISBN: 9780521773072
ISBN 10: 0521773075
Pages: 342
Publication Date: 09 May 2001
Audience: Professional and scholarly , College/higher education , Undergraduate , Primary
Format: Hardback
Publisher's Status: Active

Reviews for Statistical Mechanics of Learning

'... 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.' Zentralblatt fur Mathematik und ihre Grenzgebiete Mathematics Abstracts ...they give an exceptionally lucid account not only of what we have learned but also of how the calculations are done...Given the highly techinical nature of the calculations, the presentation is miraculously clear, even elegant. Although I have worked on these problems myself, I found, in reading the chapters, that I kept getting new insights...I highly recommend this book as a way to learn what statistical mathematics can say about an important basic problem. Physics Today