我来给个详细介绍
【书名】 Neural Networks for Pattern Recognition
【作者】CHRISTOPHER M. BISHOP
【出版社】Oxford University Press
【版本】
【出版日期】1996
【文件格式】PDF
【文件大小】22.4 MB
【页数】504 Pages
【ISBN出版号】ISBN-10: 0198538642 ISBN-13: 978-0198538646
【资料类别】计量经济学,统计学,
【市面定价】82.80 Dollars (Amazon Paperback)
【扫描版还是影印版】影印版
【是否缺页】完整
【关键词】Neutral Nwtwork, target coding scheme, logistic sigmoid activation function, outer product approximation, Monte Carlo
【内容简介】
This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.
【目录】
CONTENTS
1 Statistical Pattern Recognition 1
2 Probability Density Estimation 33
3 Single-Layer Networks 77
4 The Multi-layer Perceptron • 116
5 Radial Basis Functions 164
6 Error Functions 194
7 Parameter Optimization Algorithms 253
8 Pre-processing and Feature Extraction 295
9 Learning and Generalization 332
10 Bayesian Techniques 385
【书评】很不错的书,看目录就知道