Neural Network Models (Statistical Associates "Blue Book" Series Book 46)

A graduate level introduction to and illustrated tutorial on neural network analysis.



Why we think it is important: Neural network analysis is a valuable tool for prediction of continuous target variables or classification of categorical target variables. It is robust for noisy and missing data, and is particularly useful when nonlinear relationships which cannot be addressed through data transformations or generalized link functions exist in the data.



New title in 2014:

* A thorough discussion of implementation of neural network mod4els, including multi-layer perceptron (MLP or backpropagation) and radial basis function (RBF) models.

* Illustrates neural network modeling using SPSS and SAS, and explains Stata limitations.

* Illustrates use of neural network modeling with SAS Enterprise Miner, which allows automated comparison of fit across various neural and regression models. As such this volume provides an introduction to use of the SAS EM data mining system.

* Worked examples with links to data used.



Below is the unformatted table of contents.





NEURAL NETWORK MODELS

Overview 6

Data examples 8

Artificial neural network software 9

Key concepts and terms 10

Abbreviations 10

Types of artificial neural network models 10

Multilayer perceptron (MLP) models 10

Radial basis function (RBF) models 11

Kohonen self-organizing models 11

Networks, nodes, and weights 13

Models 16

Datasets 16

Training, recall, and learning 17

Training dataset considerations 18

Setting learning parameters 20

Convergence 22

Activation functions 23

Normalization 24

Multilayer perceptron (backpropagation) models 25

Overview 25

MLP models in SPSS 26

SPSS input for ANN-MLP 26

SPSS output for ANN-MLP 40

MLP models in SAS Enterprise Miner 49

Overview 49

Overview of SAS Enterprise Miner steps 50

MLP flow chart 60

Data Partition 60

Modeling 61

Architecture 62

Optimization 63

Model selection criterion 65

Output 66

Model Comparison 73

Scoring 75

MLP Models in SAS PROC NEURAL 77

Overview 77

SAS syntax 77

SAS output 78

Autoneural models in SAS 84

Overview 84

Example 85

Radial basis function models 86

Overview 86

RBF models, data order, and randomization 87

ANN-RBF models in SPSS 88

SPSS input for ANN-RBF 88

SPSS output for ANN-RBF 97

ANN-RBF models in SAS 109

Overview 109

Example using SAS Enterprise Miner 110

Neural network modeling in Stata 112

Assumptions 112

Data level 112

Adequate variance 112

Representative training cases 113

Randomization 113

Few outliers 113

Frequently asked questions 113

What are the “NIST Studies” in relation to ANN? 113

What is a backpropagation model? 114

How can I tell if my results are significant? 116

How can I improve the generalization of my model? 117

Explain neural weights 118

Explain activation (transfer) functions 119

Explain settings for learning rate parameters 121

What are strategies for model complexity vs. model parsimony? 123

Explain quartile analysis 124

Is generalized ANN available? 125

Do I need to transform my input variables? 125

Do I need to standardize my input variables? 125

How should I code binary variables? 127

How do I handle “DK= Don’t Know” and similar codes for my dependent variable? 127

What are pretrained networks? 128

What is a PNN model? 128

What is a GRNN model? 128

What are “constructive algorithms” in ANN-RBF? 129

What software is available to implement ANN models? 129

What are some drawbacks to use of ANN? 129

Bibliography 132

Appendix A: SAS Optimized Data Step Code 136

Appendix B: SAS Results for the “Score” node 141

Pagecount: 144