Neural Networks for Pattern Recognition

Przednia okładka
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
 

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Spis treści

1 Statistical Pattern Recognition
1
2 Probability Density Estimation
33
3 SingleLayer Networks
77
4 The Multilayer Perceptron
116
5 Radial Basis Functions
164
6 Error Functions
194
7 Parameter Optimization Algorithms
253
8 Preprocessing and Feature Extraction
295
10 Bayesian Techniques
385
Symmetric Matrices
440
Gaussian Integrals
444
Lagrange Multipliers
448
Calculus of Variations
451
Principal Components
454
References
457
Index
477

9 Learning and Generalization
332

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Informacje o autorze (1995)

Chris Bishop is at Aston University.

Informacje bibliograficzne