Neural Networks
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This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modeling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.
Hervé Abdi was born in France where he grew up. He received an M.S. in Psychology from the University of Franche-Comté (France) in 1975, an M.S. (D.E.A.) in Economics from the University of Clermond-Ferrand (France) in 1976, an M.S. (D.E.A.) in Neurology from the University Louis Pasteur in Strasbourg (France) in 1977, and a Ph.D. in Mathematical Psychology from the University of Aix-en-Provence (France) in 1980. He was an assistant professor in the University of Franche-Comté (France) in 1979, an associate professor in the University of Bourgogne at Dijon (France) in 1983, a full professor in the University of Bourgogne at Dijon (France) in 1988. He is currently a full professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas and an adjunct professor of radiology at the University of Texas Southwestern Medical Center at Dallas. He was twice a Fulbright scholar. He has been also a visiting scientist or professor in in the Rotman Institute (Toronto University), in Brown University, and in the Universities of Chuo (Japan), Dijon (France), Geneva (Switzerland), Nice Sophia Antipolis (France), and Paris 13 (France). His recent work is concerned with face and person perception, odor perception, and with computational modeling of these processes. He is also developing statistical techniques to analyze the structure of large data sets as found, for example, in brain imaging and sensory evaluation (e.g., principal component analysis, correspondence analysis, PLS-Regression, STATIS, DISTATIS, discriminant correspondence analysis, multiple factor analysis, multi-table analysis, additive tree representations, ...). In the past decade, he has published over 80 papers (plus 5 books and 3 edited volumes) on these topics. He teaches or has taught classes in cognition, computational modeling, experimental design, multivariate statistics, and the analysis of brain imaging data.
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