Neural Networks: (Quantitative Applications in the Social Sciences)

Neural networks, adaptive statistical models based on an analogy with the structure of the brain, can be used to estimate the parameters of some population using one (or a few) exemplars at a time. This book introduces readers to the basic models of neural networks and compares and contrasts these models using other statistical models. Through the use of examples that can be computed by hand or with a simple calculator, the authors describe and explain the various models.

Table of Contents:
Introduction The Perceptron Linear Autoassociative Memories Linear Heteroassociative Memories Error Backpropagation Useful References

About the Author :
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.