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| 001 | 000000767281 | |
| 005 | 20020513175429 | |
| 008 | 980710s1998 enka b 001 0 eng c | |
| 010 | ▼a 98061097 | |
| 020 | ▼a 0761957308 | |
| 020 | ▼a 0761957316 (pbk.) | |
| 040 | ▼a UkNcU ▼c EUN ▼d DLC ▼d UKM ▼d OCL ▼d LVB ▼d 211009 | |
| 042 | ▼a pcc | |
| 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 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
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
