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Statistical Mechanics of Learning

$150.00 (X)

  • Date Published: April 2001
  • availability: Available
  • format: Hardback
  • isbn: 9780521773072

$ 150.00 (X)

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About the Authors
  • The effort to build machines that are able to learn and undertake tasks such as datamining, image processing and pattern recognition has led to the development of artificial neural networks in which learning from examples may be described and understood. The contribution to this subject made over the past 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.

    • First book to review the progress in the last decade in the statistical mechanics applied to the exciting area of learning
    • Detailed and self-contained account that can be used either as a quick reference or as an introduction for newcomers
    • Suitable for a broad, interdisciplinary audience
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    Reviews & endorsements

    "...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

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    Product details

    • Date Published: April 2001
    • format: Hardback
    • isbn: 9780521773072
    • length: 342 pages
    • dimensions: 244 x 170 x 21 mm
    • weight: 0.75kg
    • contains: 1 table 136 exercises
    • availability: Available
  • Table of Contents

    1. Getting started
    2. Perceptron learning - basics
    3. A choice of learning rules
    4. Augmented statistical mechanics formulation
    5. Noisy teachers
    6. The storage problem
    7. Discontinuous learning
    8. Unsupervised learning
    9. On-line learning
    10. Making contact with statistics
    11. A bird's eye view: multifractals
    12. Multilayer networks
    13. On-line learning in multilayer networks
    14. What else?
    Appendix A. Basic mathematics
    Appendix B. The Gardner analysis
    Appendix C. Convergence of the perceptron rule
    Appendix D. Stability of the replica symmetric saddle point
    Appendix E. 1-step replica symmetry breaking
    Appendix F. The cavity approach
    Appendix G. The VC-theorem.

  • Authors

    A. Engel, Otto-von-Guericke-Universität Magdeburg, Germany

    C. Van den Broeck, Limburgs Universitair Centrum, Belgium

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