Pattern Recognition and Neural Networks

Author: Brian D. Ripley
Edition: 1
Publisher: Cambridge University Press
Binding: Paperback
ISBN: 0521717701
Category: Programming
List Price: $ 55.00
Price: $ 40.60
You Save: 26%

Pattern Recognition and Neural Networks

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. Programming books Pattern Recognition and Neural Networks pdf. 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.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. Download books Pattern Recognition and Neural Networks pdf via mediafire, 4shared, rapidshare.

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Pattern recognition and neural networks: Brian D. Ripley

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Categories: Neural networks (Computer science), Pattern recognition systems, Neural networks (Computer science). Contributors: Brian D. Ripley - Author. Format: Hardcover

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Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior by David Scarborough, Mark John Somers

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Categories: Neural networks (Computer science), Pattern recognition systems, Neural networks (Computer science. Contributors: Vladimir Cherkassky - Author. Format: Paperback

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Download Pattern Recognition and 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.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. Download free Pattern Recognition and Neural Networks pdf