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Theoretical Advances in Neural Computation and Learning (Hardcover, 1994 ed.) Loot Price: R4,610
Discovery Miles 46 100
Theoretical Advances in Neural Computation and Learning (Hardcover, 1994 ed.): Vwani Roychowdhury, Kai-Yeung Siu, Alon Orlitsky

Theoretical Advances in Neural Computation and Learning (Hardcover, 1994 ed.)

Vwani Roychowdhury, Kai-Yeung Siu, Alon Orlitsky

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Loot Price R4,610 Discovery Miles 46 100 | Repayment Terms: R432 pm x 12*

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Theoretical Advances in Neural Computation and Learning brings together in one volume some of the recent advances in the development of a theoretical framework for studying neural networks. A variety of novel techniques from disciplines such as computer science, electrical engineering, statistics, and mathematics have been integrated and applied to develop ground-breaking analytical tools for such studies. This volume emphasizes the computational issues in artificial neural networks and compiles a set of pioneering research works, which together establish a general framework for studying the complexity of neural networks and their learning capabilities. This book represents one of the first efforts to highlight these fundamental results, and provides a unified platform for a theoretical exploration of neural computation. Each chapter is authored by a leading researcher and/or scholar who has made significant contributions in this area. Part 1 provides a complexity theoretic study of different models of neural computation. Complexity measures for neural models are introduced, and techniques for the efficient design of networks for performing basic computations, as well as analytical tools for understanding the capabilities and limitations of neural computation are discussed. The results describe how the computational cost of a neural network increases with the problem size. Equally important, these results go beyond the study of single neural elements, and establish to computational power of multilayer networks. Part 2 discusses concepts and results concerning learning using models of neural computation. Basic concepts such as VC-dimension and PAC-learning are introduced, and recentresults relating neural networks to learning theory are derived. In addition, a number of the chapters address fundamental issues concerning learning algorithms, such as accuracy and rate of convergence, selection of training data, and efficient algorithms for learning useful classes of mappings.

General

Imprint: Springer
Country of origin: Netherlands
Release date: November 1994
First published: 1994
Editors: Vwani Roychowdhury • Kai-Yeung Siu • Alon Orlitsky
Dimensions: 235 x 155 x 26mm (L x W x T)
Format: Hardcover
Pages: 468
Edition: 1994 ed.
ISBN-13: 978-0-7923-9478-5
Categories: Books > Computing & IT > Applications of computing > Databases > General
Books > Computing & IT > Applications of computing > Artificial intelligence > General
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LSN: 0-7923-9478-X
Barcode: 9780792394785

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