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Theoretical Advances in Neural Computation and Learning PDF Download. Download free ebook of Theoretical Advances in Neural Computation and Learning in PDF format or read online by Kai-Yeung Siu 9780792394785 Published on 1994-11-30 by Springer Science & Business Media

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 recent results 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.

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Book ID of Theoretical Advances in Neural Computation and Learning's Books is 1-1xjgAk2_cC, Book which was written by Kai-Yeung Siu have ETAG "cE8+MfXQ1xA"

Book which was published by Springer Science & Business Media since 1994-11-30 have ISBNs, ISBN 13 Code is 9780792394785 and ISBN 10 Code is 079239478X

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Download Theoretical Advances in Neural Computation and Learning PDF Free

Download Theoretical Advances in Neural Computation and Learning Books Free

Download Theoretical Advances in Neural Computation and Learning Free

Download Theoretical Advances in Neural Computation and Learning PDF

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