Tuesday 13 April 2010

Netlab

Netlab



Netlab



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This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems. Features of particular interest include: - A NETLAB toolbox which is freely available- Worked examples, demonstration programs and over 100 graded exercises- Cutting edge research made accessible for the first time in a highly usable form- Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian ProcessesAlthough primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions a
Netlab new edition

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NETLAB: Algorithms for Pattern Recognition - Ian T. Nabney

Netlab Algorithms for Pattern Recognition, ISBN-13: 9781852334406, ISBN-10: 1852334401

Powered by Frooition Pro Click here to view full size. Full Size Image Click to close full size. Netlab - Book NEW Author(s): Ian T. Nabney Format: Paperback # Pages: 420 ISBN-13: 9781852334406 Published: 12/01/2001 Language: English Weight: 1.35 pounds Brand new book. About Us Payment Shipping Customer Service FAQs Welcome to MovieMars All items are Brand New. We offer unbeatable prices, quick shipping times and a wide selection second to none. Purchases come with a 30-day Satisfaction Guarant

"Getting the most out of neural networks and related data modelling techniques is the purpose of this book. The text, with the accompanying Netlab toolbox, provides all the necessary tools and knowledge. Throughout, the emphasis is on methods that are relevant to the practical application of neural networks to pattern analysis problems. All parts of the toolbox interact in a coherent way, and implementations and descriptions of standard statistical techniques are provided so that they can be used as benchmarks against which more sophisticated algorithms can be evaluated. Plenty of examples and



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