Pattern Recognition by Self-Organizing Neural Networks (Bradford Books)

Carpenter, Gail A., Grossberg, Stephen, eds.

ISBN 10: 0262031760 / ISBN 13: 9780262031769

Edité par A Bradford Book, 1991

A propos de cet article

709 pp., Hardcover, ex library, else text and binding clean and tight, lacks dust jacket. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

N° de réf. du vendeur ZB1317186

Détails bibliographiques

• Titre: Pattern Recognition by Self-Organizing Neural Networks (Bradford Books)
• Éditeur: A Bradford Book
• Date de parution: 1991
• Langue: anglais
• ISBN à 10 chiffres: 0262031760
• ISBN à 13 chiffres: 9780262031769
• Reliure: Couverture rigide
• État de l'article: Good

A propos de ce titre

Synopsis

Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general.

Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections.

Introductory survey articles provide a framework for understanding the many models involved in various approaches to studying neural networks. These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designing adaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks, selforganizing pattern recognition systems whose top-down template feedback signals guarantee their stable learning in response to arbitrary sequences of input patterns. In Part 4, articles describe embedding ART modules into larger architectures and provide experimental evidence from neurophysiology, event-related potentials, and psychology that support the prediction that ART mechanisms exist in the brain.

Contributors
J.-P. Banquet, G.A. Carpenter, S. Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C. von der Malsburg, C.L. Winter

À propos de l?auteur

Gail A. Carpenter is Professor of Mathematics and Cognitive and Neural Systems and Director of the CNS Technology Lab at Boston University.

Stephen Grossberg is Professor of Mathematics, Psychology, and Biomedical Engineering and Director of the Center for Adaptive Systems at Boston University.