Neural Networks: A Comprehensive Foundation (International Edition)

Haykin, Simon O.

ISBN 10: 0139083855 / ISBN 13: 9780139083853

Editore: Pearson, 1997

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The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Codice articolo GOR002175645

Dati bibliografici

• Titolo: Neural Networks: A Comprehensive Foundation (International Edition)
• Editore: Pearson
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0139083855
• ISBN 13: 9780139083853
• Rilegatura: Paperback
• Edizione: seconda edizione
• Condizione: Very Good

Descrizione articolo

Riassunto

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.

Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

Dalla quarta di copertina

Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

NEW TO THIS EDITION

• NEW―New chapters now cover such areas as:
  • Support vector machines.
  • Reinforcement learning/neurodynamic programming.
  • Dynamically driven recurrent networks.
  • NEW-End―of-chapter problems revised, improved and expanded in number.
  FEATURES
  • Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications.
  • Detailed analysis of back-propagation learning and multi-layer perceptrons.
  • Explores the intricacies of the learning process―an essential component for understanding neural networks.
  • Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
  • Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice.
  • Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
  • Includes a detailed and extensive bibliography for easy reference.
  • Computer-oriented experiments distributed throughout the book
  • Uses Matlab SE version 5.