Statistical Mechanics of Learning

A. Engel, C. Van den Broeck (Autoren)

Buch | Softcover

344 Seiten

2001
Cambridge University Press (Verlag)
978-0-521-77479-6 (ISBN)

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Artificial neural networks provide a simple framework for describing learning from examples. This coherent account of important concepts and techniques of statistical mechanics and their application to learning theory comes with background material in mathematics and physics, plus many examples and exercises, making it ideal for courses, self-teaching, or reference.

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.

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.

Erscheint lt. Verlag	29.3.2001
Zusatzinfo	Worked examples or Exercises; 1 Tables, unspecified
Verlagsort	Cambridge
Sprache	englisch
Maße	170 x 244 mm
Gewicht	550 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	0-521-77479-9 / 0521774799
ISBN-13	978-0-521-77479-6 / 9780521774796
Zustand	Neuware