Overview
- Editors:
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Vwani Roychowdhury
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Purdue University, West Lafayette, USA
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Kai-Yeung Siu
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University of California, Irvine, USA
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Alon Orlitsky
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AT&T Bell Laboratories, New Jersey, USA
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Table of contents (13 chapters)
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Front Matter
Pages i-xxiv
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Computational Complexity of Neural Networks
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- Vwani Roychowdhury, Kai-Yeung Siu, Alon Orlitsky
Pages 3-36
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- W. Maass, G. Schnitger, E. D. Sontag
Pages 127-151
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Learning and Neural Networks
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Front Matter
Pages 241-241
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- Bhaskar DasGupta, Hava T. Siegelmann, Eduardo Sontag
Pages 357-389
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- Babak Hassibi, Ali H. Sayed, Thomas Kailath
Pages 425-447
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Back Matter
Pages 463-468
About this book
For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.
Editors and Affiliations
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Purdue University, West Lafayette, USA
Vwani Roychowdhury
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University of California, Irvine, USA
Kai-Yeung Siu
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AT&T Bell Laboratories, New Jersey, USA
Alon Orlitsky
