Pattern Recognition by Self-Organizing Neural Networks

Gail A. Carpenter

ISBN 10: 0262031760 / ISBN 13: 9780262031769

Editore: Massachusetts Institute of Technology, 1991

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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.

Codice articolo 29c553bc-d74e-4d93-9e5c-06d9fef1a028

Dati bibliografici

• Titolo: Pattern Recognition by Self-Organizing Neural Networks
• Editore: Massachusetts Institute of Technology
• Data di pubblicazione: 1991
• Lingua: Inglese
• ISBN 10: 0262031760
• ISBN 13: 9780262031769
• Rilegatura: Hardcover
• Condizione: As New

Descrizione articolo

Riassunto

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

Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms 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.