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Neural Network Models (Statistical Associates "Blue Book" Series Book 46) by [Garson, G. David]

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

by G. David Garson (Author)

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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
Overview6
Data examples8
Artificial neural network software9
Key concepts and terms10
Abbreviations10
Types of artificial neural network models10
Multilayer perceptron (MLP) models10
Radial basis function (RBF) models11
Kohonen self-organizing models11
Networks, nodes, and weights13
Models16
Datasets16
Training, recall, and learning17
Training dataset considerations18
Setting learning parameters20
Convergence22
Activation functions23
Normalization24
Multilayer perceptron (backpropagation) models25
Overview25
MLP models in SPSS26
SPSS input for ANN-MLP26
SPSS output for ANN-MLP40
MLP models in SAS Enterprise Miner49
Overview49
Overview of SAS Enterprise Miner steps50
MLP flow chart60
Data Partition60
Modeling61
Architecture62
Optimization63
Model selection criterion65
Output66
Model Comparison73
Scoring75
MLP Models in SAS PROC NEURAL77
Overview77
SAS syntax77
SAS output78
Autoneural models in SAS84
Overview84
Example85
Radial basis function models86
Overview86
RBF models, data order, and randomization87
ANN-RBF models in SPSS88
SPSS input for ANN-RBF88
SPSS output for ANN-RBF97
ANN-RBF models in SAS109
Overview109
Example using SAS Enterprise Miner110
Neural network modeling in Stata112
Assumptions112
Data level112
Adequate variance112
Representative training cases113
Randomization113
Few outliers113
Frequently asked questions113
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 weights118
Explain activation (transfer) functions119
Explain settings for learning rate parameters121
What are strategies for model complexity vs. model parsimony?123
Explain quartile analysis124
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
Bibliography132
Appendix A: SAS Optimized Data Step Code136
Appendix B: SAS Results for the “Score” node141
Pagecount: 144

Length: 158 pages

Enhanced Typesetting: Enabled

Page Flip: Enabled

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Product details

File Size: 6718 KB
Print Length: 158 pages
Publisher: Statistical Associates Publishers; 2014 edition (January 1, 2014)
Publication Date: January 1, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00HORW3HA
Text-to-Speech: Enabled

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Word Wise: Not Enabled
Lending: Enabled
Screen Reader: Supported

Enhanced Typesetting: Enabled

Amazon Best Sellers Rank: #727,595 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
- #279 in Kindle Store > Kindle eBooks > Reference > Writing, Research & Publishing Guides > Research
- #303 in Kindle Store > Kindle eBooks > Nonfiction > Politics & Social Sciences > Social Sciences > Research
- #813 in Books > Reference > Writing, Research & Publishing Guides > Research

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A decent, fairly non-technical introduction.

ByDaleOon July 22, 2016

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A decent, fairly non-technical introduction. It's been a while since I read it, but it stepped one through some software, and explained the concepts behind neural networks, in a not excessively technical way. It's the sort of book to help you use and understand the application software, but not write the algorithms.

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Five Stars

BySzymon Szumia³on May 3, 2017

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Very easy to understand. Precise in the software description.

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