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Information-Theoretic Aspects of Neural Networks, by P. S. Neelakanta
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Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.
Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:
Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.
- Sales Rank: #3728762 in Books
- Brand: Brand: CRC Press
- Published on: 1999-03-30
- Original language: English
- Number of items: 1
- Dimensions: 9.75" h x 6.50" w x 1.00" l, 1.50 pounds
- Binding: Hardcover
- 416 pages
- Used Book in Good Condition
Most helpful customer reviews
2 of 2 people found the following review helpful.
A good book dealing with stochastical neural networks.
By Karthik Sundaram
This book has dealt with in depth the various entropy error measures that can be used for the neural networks. The Minimum entropy error measures and the Maximum entropy error functions like the Kapur measures, Sharma-Mittal error measures are discussed as an alternative to the classical square error methods when the input and the teacher values are stochastical variables. The book also has good introduction and is well written. The book has experimental data to support the claims made by the author.
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