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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.
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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)
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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 New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Back Cover | Surprise Me!
Citations (learn more)
This book cites 63
books:
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100
books
cite this book:
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- Nonrecursive Causal Models (Quantitative Applications in the Social Sciences) by Dr. William D. Berry in Back Matter
- Causal Modeling (Quantitative Applications in the Social Sciences) by Herbert B. Asher in Back Matter
- Stochastic Parameter Regression Models (Quantitative Applications in the Social Sciences) by Paul Newbold in Back Matter
- Research Designs (Quantitative Applications in the Social Sciences) by Paul E. (Elliot) Spector in Back Matter
- Analyzing Panel Data (Quantitative Applications in the Social Sciences) by Gregory B. Markus in Back Matter
See all 63 books this book cites
- Computational Maps in the Visual Cortex by Risto Miikkulainen on 5 pages
- Frontiers of Complexity: The Search for Order in a Chaotic World by Roger Highfield in Back Matter (1), Back Matter (2), and Back Matter (3)
- How We Learn; How We Remember: Toward an Understanding of Brain and Neural Systems : Selected Papers of Leon N. Cooper (World Scientific Series in 20th Century Physics, Vol 10) by Leon N. Cooper on page 197, page 231, and page 308
- Radial Basis Function Networks 2: New Advances in Design (Studies in Fuzziness and Soft Computing) (v. 2) by Robert J. Howlett on page 139, page 141, and page 315
- Models of Neural Networks II: Temporal Aspects of Coding and Information Processing in Biological Systems (Physics of Neural Networks) (v. 2) by Eytan Domany in Front Matter, page 171, and Back Matter
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