Pattern recognition and neural networks

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. Amazon.com 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. This 1996 Book Is A Reliable Account Of The Statistical Framework For Pattern Recognition And Machine Learning. With Unparalleled Coverage And A Wealth Of Case-studies This Book Gives Valuable Insight Into Both The Theory And The Enormously Diverse Applications (which Can Be Found In Remote Sensing, Astrophysics, Engineering And Medicine, For Example). So That Readers Can Develop Their Skills And Understanding, Many Of The Real Data Sets Used In The Book Are Available From The Author's Website: Www.stats.ox.ac.uk/~ripley/prbook/. For The Same Reason, Many Examples Are Included To Illustrate Real Problems In Pattern Recognition. Unifying Principles Are Highlighted, And The Author Gives An Overview Of The State Of The Subject, Making The Book Valuable To Experienced Researchers In Statistics, Machine Learning/artificial Intelligence And Engineering. The Clear Writing Style Means That The Book Is Also A Superb Introduction For Non-specialists. B.d. Ripley. Includes Bibliographical References (p. [355]-390) And Indexes.

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.

The clearest explanation of the statistical framework for pattern recognition and machine learning, now in paperback. This book is primarily about pattern recognition, which covers a wide range of activities from many walks of life.