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Limitations and future trends in neural computation / edited by Sergey Ablameyko [and others].

By: Contributor(s): Material type: TextTextSeries: NATO science series. Series III, Computer and systems sciences ; ; v. 186.Publication details: Amsterdam ; Burke, VA : IOS Press ; Tokyo : Ohmsha, ©2003.Description: 1 online resource (ix, 245 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 1417511508
  • 9781417511501
  • 1601294433
  • 9781601294432
  • 9781586033248
  • 1586033247
  • 9784274905810
  • 4274905810
  • 1280505877
  • 9781280505874
  • 9786610505876
  • 661050587X
  • 6000004788
  • 9786000004781
Subject(s): Genre/Form: Additional physical formats: Print version:: Limitations and future trends in neural computation.DDC classification:
  • 006.3/2 22
LOC classification:
  • QA76.87 .N37 2001eb
Online resources:
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.
Summary: 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.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Computers Available
Total holds: 0

"Published in cooperation with NATO Scientific Affairs Division."

Includes bibliographical references and index.

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.

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.

Print version record.

English.

Added to collection customer.56279.3

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