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Item Details
| Title:
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BACKPROPAGATION
THEORY, ARCHITECTURES AND APPLICATIONS |
| By: |
Yves Chauvin (Editor), David E. Rumelhart (Editor) |
| Format: |
Hardback |
| List price:
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£145.00 |
| Our price: |
£130.50 |
| Discount: |
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| You save:
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£14.50 |
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| ISBN 10: |
080581258X |
| ISBN 13: |
9780805812589 |
| Availability: |
Usually dispatched within 1-3 weeks.
 Delivery
rates
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| Stock: |
Currently 0 available |
| Publisher: |
TAYLOR & FRANCIS INC |
| Pub. date: |
1 February, 1995 |
| Series: |
Developments in Connectionist Theory S. |
| Pages: |
576 |
| Description: |
Composed of three sections, this book presents the training algorithm for neural networks: backpropagation. The first presents the theory and principles; the second a number of network architectures; and the third shows how these can be applied to a number of different fields. |
| Synopsis: |
Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The second presents a number of network architectures that may be designed to match the general concepts of Parallel Distributed Processing with backpropagation learning. Finally, the third section shows how these principles can be applied to a number of different fields related to the cognitive sciences, including control, speech recognition, robotics, image processing, and cognitive psychology. The volume is designed to provide both a solid theoretical foundation and a set of examples that show the versatility of the concepts. Useful to experts in the field, it should also be most helpful to students seeking to understand the basic principles of connectionist learning and to engineers wanting to add neural networks in general -- and backpropagation in particular -- to their set of problem-solving methods. |
| Publication: |
US |
| Imprint: |
Lawrence Erlbaum Associates Inc |
| Returns: |
Returnable |
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