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Neural Networks (Quantitative Applications in the Social Sciences)
 
 
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Neural Networks (Quantitative Applications in the Social Sciences) [Paperback]

Hervé Abdi (Author), Dominique Valentin (Author), Betty Edelman (Author)
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Book Description

0761914404 978-0761914402 December 9, 1998 1

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.


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Editorial Reviews

About the Author

Herve Abdi was born in France where he grew up. He received an M.S. in Psychology from the University of Franche-Comte (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-Comte (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.

Product Details

  • Paperback: 96 pages
  • Publisher: Sage Publications, Inc; 1 edition (December 9, 1998)
  • Language: English
  • ISBN-10: 0761914404
  • ISBN-13: 978-0761914402
  • Product Dimensions: 8.4 x 5.3 x 0.2 inches
  • Shipping Weight: 4.8 ounces (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,650,751 in Books (See Top 100 in Books)

More 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, e.g., in brain imaging and sensory evaluation (e.g., PLS-Regression, STATIS, DISTATIS, discriminant correspondence analysis, multiple factor 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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10 of 10 people found the following review helpful:
5.0 out of 5 stars Short but good, June 14, 2001
By A Customer
This review is from: Neural Networks (Quantitative Applications in the Social Sciences) (Paperback)
This is a short but very clear and very detailed book. It gives most of the techniques and the concepts to understand neural networks. it was my first introduction to the topic and since then I have been able to read more advanced texts and papers. I would say that this is the ideal entry point to the topic as well as an interesting book for the general reader willing to have a general overview of the field
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Inside This Book (learn more)
First Sentence:
Neural networks are adaptive statistical models based on an analogy with the structure of the brain. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
active input cells, linear heteroassociative memory, kth stimulus, one output cell, error term vector, autoassociative memory, heteroassociative memories, nonlinear unit, autoassociative memories, memory trained, threshold unit, nonlinear transfer function, unit computes, backpropagation network, hidden cell, synaptic weights, radial basis function network
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Output Supervisor, The Building Block
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