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. 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. 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.