Neural Networks for Pattern Recognition / Edition 1by Christopher M. Bishop, C. M. Bishop
Pub. Date: 01/18/1996
Publisher: Oxford University Press, USA
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various… See more details below
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
- Oxford University Press, USA
- Publication date:
- Edition description:
- New Edition
- Product dimensions:
- 9.19(w) x 6.19(h) x 1.12(d)
Table of Contents
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
A. Symmetric Matrices
B. Gaussian Integrals
C. Lagrange Multipliers
D. Calculus of Variations
E. Principal Components
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I didn't know anything about neural networks when I bought this book. It was a very hard and slow read, but on my third attempt things started to fall into place. Now I'm a whiz and this book did it all. A great review of many different angles and you can traslate it all into useful applications
Great book to read through to get full understanding of neural networks and about what what they do and how they do it. It pretty mathematical which is a GOOD thing most books about the topic give a very schematic approach to it. If you know your calculus you should be fine and even if you don't it goes over how to solve most of it. My only complaint ( not with this book but in all books that does this) is the lack of answers for the exercises. I've always liked to check my work. But thats a small problem. This book is GREAT!!! go out and get it now.
a compact book covering all the essential items. very well written. author has done a good job. this book is for technical research people.
This book helps a lot in understanding how a basic structure of neural network can be used practically in the industry. Mr.Bishop has done a good job in making it as simple as possible for people with very little background to understand it very well.
A clear, insightful description of the basic theory behind neural networks and its application to pattern recognition and function regression. It also does a good job describing problems one might encounter in using neural networks by tying together many ideas from the field of neural network with those from statistical/probability theory and regression theory.