Neural networks. A comprehensive foundation.

Haykin, Simon S.:

ISBN 10: 0139083855 / ISBN 13: 9780139083853

Edité par Upper Saddle River, Prentice-Hall, 1999

A propos de cet article

842 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. AD6295 9780139083853 Sprache: Englisch Gewicht in Gramm: 1250.

N° de réf. du vendeur 2368606

Détails bibliographiques

• Titre: Neural networks. A comprehensive foundation.
• Éditeur: Upper Saddle River, Prentice-Hall
• Date de parution: 1999
• Langue: anglais
• ISBN à 10 chiffres: 0139083855
• ISBN à 13 chiffres: 9780139083853
• Reliure: Softcover
• Édition: 2. ed., [Nachdr.], internat. ed.
• Poids: 1250 grammes
• Catalogues du vendeur:
  • Q Wirtschaftswissenschaften

A propos de ce titre

Synopsis

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.

Quatrième de couverture

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.