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Neural networks : an introductory guide for social scientists

Neural networks : an introductory guide for social scientists (Loan 1 times)

Material type
단행본
Personal Author
Garson, G. David.
Title Statement
Neural networks : an introductory guide for social scientists / G. David Garson.
Publication, Distribution, etc
London ;   Thousand Oaks, Calif. :   Sage,   1998.  
Physical Medium
vi, 194 p. : ill. ; 24 cm.
Series Statement
New technologies for social research
ISBN
0761957308 0761957316 (pbk.)
Bibliography, Etc. Note
Includes bibliographical references (p. [169]-189) and index.
Subject Added Entry-Topical Term
Neural networks (Computer science) Social sciences -- Mathematical models. Social sciences -- Data processing.
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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 B M

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



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