Read more
Zusatztext ... very enjoyable to read and often opening the reader's eye to new possibilities. This is a perfect introduction to the field for students and researchers who want to study problems in information science, including the use of physics in information processing Informationen zum Autor Professor Hidetoshi Nishimori, Department of Physics, Tokyo Institute of Technology, Oh-okayama, Meguro-ku, Tokyo 152-8551, Japan, Tel.: +81/3/57342488, Fax: +81/3/57342719, email: nishi@stat.phys.titech.ac.jp Klappentext The present book provides the reader with a general perspective of an interdisciplinary field between statistical physics and information sciences/engineering. It is virtually the only book on the subject! except for a collection of papers published fourteen years ago. The field is expanding quite rapidly! and this self-contained presentation may be expected to acquire a wide audience in physics and engineering. Zusammenfassung Spin glasses are magnetic materials. Statistical mechanics, a subfield of physics, has been a powerful tool to theoretically analyse various unique properties of spin glasses. A number of new analytical techniques have been developed to establish a theory of spin glasses. Surprisingly, these techniques have turned out to offer new tools and viewpoints for the understanding of information processing problems, including neural networks, error-correcting codes, image restoration, and optimization problems. This book is one of the first publications of the past ten years that provide a broad overview of this interdisciplinary field. Most of the book is written in a self-contained manner, assuming only a general knowledge of statistical mechanics and basic probability theory. It provides the reader with a sound introduction to the field and to the analytical techniques necessary to follow its most recent developments. Inhaltsverzeichnis 1: Mean-field theory of phase transitions 2: Mean-field theory of spin glasses 3: Replica symmetry breaking 4: Gauge theory of spin glasses 5: Error-correcting codes 6: Image restoration 7: Associative memory 8: Learning in perceptron 9: Optimization problems A. Eigenvalues of the Hessian B. Parisi equation C. Channel coding theorem D. Distribution and free energy of K-Sat References Index ...
