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Neural Networks for Pattern Recognition
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 ...more
Paperback, 504 pages
Published January 18th 1996 by Clarendon Press
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A great book on the use of neural network as they apply to pattern recognition. This book is understandably heavy on the mathematical aspects of things, but the author does so with a grain of salt. This is definitely not a mathematics book.
Most books about neural networks are silent when it comes to less obvious details of what training feedforward networks actually does, and what the result of the network represents. This book analyzes all these details by means of statistics, and consequently gives a very solid foundation for understanding neural networks. The definitive book for feedforward neural networks - extremely good! The math-heavy treatise makes it unsuitable for a beginners book, though.
This book was a decent understanding of neural networks and the math behind segmentation etc. it could have been more math heavy in my opinion but makes a good attempt to cover a more advanced concept and make it relatively approachable by someone with knowledge. Overall it was a good read.
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