Limitations and future trends in neural computation / edited by Sergey Ablameyko [and others]
This work reports critical analyses on complexity issues in the continuum setting and on generalization to new examples, which are two basic milestones in learning from examples in connectionist models. It also covers up-to-date developments in computational mathematics.
Saved in:
| Online Access: |
Full Text (via ProQuest) |
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| Corporate Author: | |
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| Format: | Conference Proceeding eBook |
| Language: | English |
| Published: |
Amsterdam ; Burke, VA : Tokyo :
IOS Press ; Ohmsha,
©2003.
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| Series: | NATO science series. Computer and systems sciences ;
v. 186. |
| Subjects: |
Table of Contents:
- Cover; Title page; Preface; Contents; Chapter 1. Continuous Problem Solving and Computational Suspiciousness; Chapter 2. The Complexity of Computing with Continuous Time Devices; Chapter 3. Energy-Based Computation with Symmetric Hopfield Nets; Chapter 4. Computational Complexity and the Elusiveness of Global Optima; Chapter 5. Impact of Neural Networks on Signal Processing and Communications; Chapter 6. From Clustering Data to Traveling as a Salesman: Empirical Risk Approximation as a Learning Theory; Chapter 7. Learning High-dimensional Data.