Elements of artificial neural networks / Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka.
Elements of artificial neural networks provides a clear introduction to neural networks for those who want to use them rather than simply study them. The authors, who have been team-teaching the material in a one-semester course over the past six years, describe most of the basic neural network mode...
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Main Authors: | , , |
Other title: | Artificial neural networks |
Format: | eBook |
Language: | English |
Published: |
Cambridge, Mass. :
MIT Press,
1997.
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Series: | Complex adaptive systems.
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Subjects: |
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245 | 1 | 0 | |a Elements of artificial neural networks / |c Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka. |
246 | 3 | 0 | |a Artificial neural networks |
264 | 1 | |a Cambridge, Mass. : |b MIT Press, |c 1997. | |
264 | 4 | |c ©1997 | |
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500 | |a "A Bradford book." | ||
504 | |a Includes bibliographical references (pages 331-338) and index. | ||
505 | 0 | 0 | |t Preface -- |g 1. |t Introduction |g 2. |t Supervised learning : single-layer networks -- |g 3. |t Supervised learning : multilayer networks I -- |g 4. |t Supervised learning : mayer networks II -- |g 5. |t Unsupervised learning -- |g 6. |t Associative models -- |g 7. |t Optimization methods -- |g appendix A. |t A little math -- |g appendix B. |t Data. |
520 | |a Elements of artificial neural networks provides a clear introduction to neural networks for those who want to use them rather than simply study them. The authors, who have been team-teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss their rationale and relative advantages. Their approach requires little mathematical or technical background. Written, from a computer science and statistics point of view, the text stresses links to contiguous fields and con serves as a first course for students in economics and management. | ||
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