Pattern Recognition and Neural Networks

Ripley, Brian D.

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ISBN 10: 0521717701 ISBN 13: 9780521717700
Published by Cambridge University Press, 2008
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Synopsis:

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Review: This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. Almost all the results have proofs that are often original. On the application side, the emphasis is on pattern recognition. Most of the examples are from real world problems. In addition to the more common types of networks, the book has chapters on decision trees and belief networks from the machine-learning field. This book is intended for use in graduate courses that teach statistics and engineering. A strong background in statistics is needed to fully appreciate the theoretical developments and proofs. However, undergraduate-level linear algebra, calculus, and probability knowledge is sufficient to follow the book.

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Bibliographic Details

Title: Pattern Recognition and Neural Networks
Publisher: Cambridge University Press
Publication Date: 2008
Binding: Soft cover
Condition: Good

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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. Seller Inventory # 166132373

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