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Neural networks : an introductory guide for social scientists

Neural networks : an introductory guide for social scientists (1회 대출)

자료유형
단행본
개인저자
Garson, G. David.
서명 / 저자사항
Neural networks : an introductory guide for social scientists / G. David Garson.
발행사항
London ;   Thousand Oaks, Calif. :   Sage,   1998.  
형태사항
vi, 194 p. : ill. ; 24 cm.
총서사항
New technologies for social research
ISBN
0761957308 0761957316 (pbk.)
서지주기
Includes bibliographical references (p. [169]-189) and index.
일반주제명
Neural networks (Computer science) Social sciences -- Mathematical models. Social sciences -- Data processing.
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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.

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 006.32 G243n 등록번호 111214535 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


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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