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Product Identifiers
PublisherCambridge University Press
ISBN-100521774799
ISBN-139780521774796
eBay Product ID (ePID)1881299
Product Key Features
Number of Pages344 Pages
Publication NameStatistical Mechanics of Learning
LanguageEnglish
Publication Year2001
SubjectIntelligence (Ai) & Semantics, Bioinformatics
TypeTextbook
AuthorA. Engel, C. Van Den Broeck
Subject AreaComputers
FormatPerfect
Dimensions
Item Height0.7 in
Item Weight19.4 Oz
Item Length9.6 in
Item Width6.7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN00-058516
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.' Zentralblatt für Mathematik und ihre Grenzgebiete Mathematics Abstracts, '… 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 fr 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
IllustratedYes
Table Of Content1. 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.
SynopsisArtificial 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., 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., Artificial neural networks, learning, statistical mechanics; background material in mathematics and physics; examples and exercises; textbook/reference.
LC Classification NumberQA76.87 .E45 2001
