Neural Networks for Pattern Recognition
(Sprache: Englisch)
This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern...
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Produktinformationen zu „Neural Networks for Pattern Recognition “
This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest applicability for neural networks. By focusing on pattern recognition, the book provides a much more extensive treatment of many topics than is available in earlier books.
Klappentext zu „Neural Networks for Pattern Recognition “
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
Inhaltsverzeichnis zu „Neural Networks for Pattern Recognition “
- 1.: Statistical pattern recognition
- 2.: Probability density estimation
- 3.: Single-layer networks
- 4.: The multi-layer perceptron
- 5.: Radial basis functions
- 6.: Error functions
- 7.: Parameter optimization algorithms
- 8.: Pre-processing and feature extraction
- 9.: Learning and generalization
- 10.: Bayesian techniques
Bibliographische Angaben
- Autor: Christopher M. Bishop
- 1995, Repr., 504 Seiten, mit Abbildungen, Maße: 15,7 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Oxford University Press
- ISBN-10: 0198538642
- ISBN-13: 9780198538646
Sprache:
Englisch
Rezension zu „Neural Networks for Pattern Recognition “
"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American StatisticalAssociation.."..should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal
"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology
"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World
"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement
"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford
"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is t
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