Statistical Mechanics of Learning
Statistical Mechanics of Learning
C. Van den Broeck , Limburgs Universitair Centrum, Belgium
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
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
Product details
April 2001Hardback
9780521773072
342 pages
244 × 170 × 21 mm
0.75kg
1 table 136 exercises
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.
