Mathematical Perspectives on Neural Networks

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9781138876293: Mathematical Perspectives on Neural Networks
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Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics.

Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as:
* Exactly what mathematical systems are used to model neural networks from the given perspective?
* What formal questions about neural networks can then be addressed?
* What are typical results that can be obtained? and
* What are the outstanding open problems?

A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.

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About the Author:

Paul Smolensky is Professor of Cognitive Science at Johns Hopkins University. He was a leading member of the PDP connectionist research group, and is the recipient of the 2005 David E. Rumelhart Prize in Cognitive Science, which is awarded annually to an individual or collaborative team making a significant contribution to the formal analysis of human cognition.

Michael C. Mozer is a Professor in the Department of Computer Science and the Institute of Cognitive Science at the University of Colorado, Boulder. In 1990 he received the Presidential Young Investigator Award from the National Science Foundation.

Paul Smolensky is Professor of Cognitive Science at Johns Hopkins University. He was a leading member of the PDP connectionist research group, and is the recipient of the 2005 David E. Rumelhart Prize in Cognitive Science, which is awarded annually to an individual or collaborative team making a significant contribution to the formal analysis of human cognition.

Michael C. Mozer is a Professor in the Department of Computer Science and the Institute of Cognitive Science at the University of Colorado, Boulder. In 1990 he received the Presidential Young Investigator Award from the National Science Foundation.

Review:

Although the material is advanced and technical, the volume has a multilayer structure including a general overview, and there are overviews of each of the main parts (including summary tables of key results). These surveys, all written by Smolensky (John Hopkins University), provide an excellent introduction to the papers and their context, making the material accessible to upper-division undergraduates, graduate students, or faculty.
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9780805812015: Mathematical Perspectives on Neural Networks (Developments in Connectionist Theory Series)

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ISBN 10:  0805812016 ISBN 13:  9780805812015
Publisher: Psychology Press, 1996
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Book Description Taylor & Francis Ltd, United Kingdom, 2015. Paperback. Condition: New. Language: English. Brand new Book. Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, this book has been assembled to present a sample of these results, and to fill in the necessary background, in such areas as computability theory, computational complexity theory, the theory of analog computation, stochastic processes, dynamical systems, control theory, time-series analysis, Bayesian analysis, regularization theory, information theory, computational learning theory, and mathematical statistics. Mathematical models of neural networks display an amazing richness and diversity. Neural networks can be formally modeled as computational systems, as physical or dynamical systems, and as statistical analyzers. Within each of these three broad perspectives, there are a number of particular approaches. For each of 16 particular mathematical perspectives on neural networks, the contributing authors provide introductions to the background mathematics, and address questions such as: * Exactly what mathematical systems are used to model neural networks from the given perspective? * What formal questions about neural networks can then be addressed? * What are typical results that can be obtained? and * What are the outstanding open problems? A distinctive feature of this volume is that for each perspective presented in one of the contributed chapters, the first editor has provided a moderately detailed summary of the formal results and the requisite mathematical concepts. These summaries are presented in four chapters that tie together the 16 contributed chapters: three develop a coherent view of the three general perspectives -- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field. Seller Inventory # BTE9781138876293

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