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