Neural Networks for Pattern Recognition
Price: 480.00 INR
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ISBN:
9780195667998
Publication date:
22/08/2005
Paperback
504 pages
241.0x159.0mm
Price: 480.00 INR
We sell our titles through other companies
Disclaimer :You will be redirected to a third party website.The sole responsibility of supplies, condition of the product, availability of stock, date of delivery, mode of payment will be as promised by the said third party only. Prices and specifications may vary from the OUP India site.
ISBN:
9780195667998
Publication date:
22/08/2005
Paperback
504 pages
241.0x159.0mm
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.
Suitable for: This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.
Rights: Indian Territory Rights (No Agent)
Description
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical 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.
Table of contents
Chapter 1. Statistical pattern recognition
Chapter 2. Probability density estimation
Chapter 3. Single-layer networks
Chapter 4. The multi-layer perceptron
Chapter 5. Radial basis functions
Chapter 6. Error functions
Chapter 7. Parameter optimization algorithms
Chapter 8. Pre-processing and feature extraction
Chapter 9. Learning and generalization
Chapter 10.Bayesian techniques
Description
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical 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.
Read MoreTable of contents
Chapter 1. Statistical pattern recognition
Chapter 2. Probability density estimation
Chapter 3. Single-layer networks
Chapter 4. The multi-layer perceptron
Chapter 5. Radial basis functions
Chapter 6. Error functions
Chapter 7. Parameter optimization algorithms
Chapter 8. Pre-processing and feature extraction
Chapter 9. Learning and generalization
Chapter 10.Bayesian techniques
