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| 008 | 980710s1998 enka b 001 0 eng c | |
| 010 | ▼a 98061097 | |
| 020 | ▼a 0761957308 | |
| 020 | ▼a 0761957316 (pbk.) | |
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| 049 | 1 | ▼l 111214535 |
| 050 | 0 0 | ▼a QA76.87 ▼b .G37 1998 |
| 082 | 0 0 | ▼a 006.3/2 ▼2 21 |
| 090 | ▼a 006.32 ▼b G243n | |
| 100 | 1 | ▼a Garson, G. David. |
| 245 | 1 0 | ▼a Neural networks : ▼b an introductory guide for social scientists / ▼c G. David Garson. |
| 260 | ▼a London ; ▼a Thousand Oaks, Calif. : ▼b Sage, ▼c 1998. | |
| 300 | ▼a vi, 194 p. : ▼b ill. ; ▼c 24 cm. | |
| 440 | 0 | ▼a New technologies for social research |
| 504 | ▼a Includes bibliographical references (p. [169]-189) and index. | |
| 650 | 0 | ▼a Neural networks (Computer science) |
| 650 | 0 | ▼a Social sciences ▼x Mathematical models. |
| 650 | 0 | ▼a Social sciences ▼x Data processing. |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 006.32 G243n | Accession No. 111214535 (1회 대출) | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
Neural networks have influenced many areas of research but have only just started to be utilized in social science research. Neural Networks provides the first accessible introduction to this analysis as a powerful method for social scientists. It provides numerous studies and examples that illustrate the advantages of neural network analysis over other quantitative and modeling methods in wide-spread use among social scientists. The author, G. David Garson, presents the methods in an accessible style for the reader who does not have a background in computer science. Features include an introduction to the vocabulary and framework of neural networks, a concise history of neural network methods, a substantial review of the literature, detailed neural network applications in the social sciences, coverage of the most common alternative neural network models, methodological considerations in applying neural networks, examples using the two leading software packages for neural network analysis, and numerous illustrations and diagrams. This introductory guide to using neural networks in the social sciences will enable students, researchers, and professionals to utilize these important new methods in their research and analysis.
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Table of Contents
CONTENTS 1 Introduction to Neural Network Analysis = 1 The Case for Neural Network Analysis = 8 Obstacles to the Spread of Neural Network Analysis in the Social Sciences = 16 Uses of Neural Network Analysis = 17 2 The Terminology of Neural Network Analysis = 23 Neural Networks = 24 Data = 27 Data Sets = 27 Models = 28 3 The Backpropagation Model = 37 Learning Rules = 37 Backpropagation Process = 42 Example : XOR Problem = 49 Learning Algorithms = 50 Backpropagation Model Variants = 54 4 Alternative Network Paradigms = 59 Generalized Regression Neural Network (GRNN) Models = 59 Probabilistic Neural Network (PNN) Models = 60 Radial Basis Function (RBF) Models = 62 Group Method of Data Handling (GMDH) of Polynmial Models = 64 Adaptive Time-Delay Neural Networks (ATNN) = 66 Adaptive Resonance Theory (ART) Map Networks = 67 Bidirectional Associative Memory (BAM) Models = 70 Kohonen Self-Organizing Map Models = 71 Counterpropagation = 74 Learning Vector Quantization (LVQ) Network Models = 75 Categorizing and Learning Module (CALM) Networks = 78 Hybrid Models = 78 5 Methodological Considerations = 81 Applicability = 81 Model Complexity = 83 The Training Data Set = 87 Training Duration = 94 Determining the Transfer (Activation) Function = 96 Setting Coefficients in the Learning Rate and Learning Schedule = 100 Improving Generalization = 100 Cross-Validation = 103 Causal Interpretation with Neural Networks = 105 6 Neural Network Software = 111 Neural Connection = 112 NeuroShell 2 = 135 7 Example : Analysing Census Data with Neural Connection = 149 Data = 150 Regression = 155 Radial Basis Function Neural Model = 155 Multi-Layer Perceptron (Backpropagation) Neural Model = 156 Text Output = 158 8 Conclusion = 161 Notes = 165 References = 169 Index = 191