Backpropagation: Theory, Architectures, and Applications (Developments in Connectionist Theory Series)
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- 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.
- Copyright:
- 1995
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 576 Pages
- ISBN-13:
- 9781134775743
- Related ISBNs:
- 9780203763247, 9780805812589, 9780805812589, 9780805812596, 9780805812596
- Publisher:
- Taylor and Francis
- Date of Addition:
- 07/10/20
- Copyrighted By:
- N/A
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Psychology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Edited by:
- Yves Chauvin
- Edited by:
- David E. Rumelhart