Neural Network Modeling using SAS Enterprise Miner
【语言】：英文
【页数/文件数】：600
【目录】：
Table of Contents
Chapter 1: Introduction
Chapter 2: Traditional Regression Modeling 1
2.1 The Purpose of Statistical Modeling ........................................................................... 3
2.2 Statistical Modeling Assumptions ............................................................................... 9
2.3 Checking for Outliers and Influential Data Points ..................................................... 23
2.4 Checking for Multicollinearity .................................................................................... 28
2.5 Modeling Assessment Statistics .................................................................................. 32
2.6 Bias-Variance Trade-off .............................................................................................. 39
Chapter 3: Neural Network Architecture 41
3.1 Single Layer Perceptron .............................................................................................. 43
3.2 Perceptron Training Algorithm .................................................................................... 44
3.3 Multiple Layer Perceptron Architecture ..................................................................... 47
3.4 An Explanation of the Neural Network Layers............................................................ 50
3.5 Relationship Between the Predictive Models .............................................................. 51
3.6 An Overview of the Neural Network Layers .............................................................. 53
3.7 An Overview of the Neural Network Architecture ..................................................... 63
3.8 The Objective Function ............................................................................................... 77
3.9 Neural Network Architectures .................................................................................... 84
3.10 Neural Network Hold out Method .............................................................................. 90
3.11 Optimization ................................................................................................................ 93
3.12 Numerical Examples of the Optimization Methods .................................................... 113
3.13 Numerical Examples of the Network Designs ............................................................ 126
3.14 Regularization Techniques .......................................................................................... 140
3.15 Pruning Inputs ............................................................................................................. 142
3.16 Interpretation of the Neural Network Inputs ............................................................... 145
3.17 Development to a Well-Designed Network Model ..................................................... 147
3.18 Advantages of Neural Network Modeling .................................................................. 149
3.19 The Default Settings to EM Neural Network Node .................................................... 153
Chapter 4: The NEURAL and DMREG Procedure 155
4.1 An Overview of the SAS Neural Network Procedure ................................................. 157
4.2 An Overview of the SAS DMREG Procedure ............................................................ 184
4.3 Multiple Linear Regression Example .......................................................................... 189
4.4 Autoregressive Time Series Example ......................................................................... 192
4.5 Basic Steps in Constructing the EM Diagram ............................................................. 195
Chapter 5: SAS Enterprise Miner 201
5.1 Opening the Enterprise Miner Application ................................................................. 203
5.2 Enterprise Miner Menu Options ................................................................................. 206
5.3 Option Settings to the EM Environment ..................................................................... 208
5.4 Enterprise Miner Projects and Diagrams .................................................................... 212
Chapter 6: Data Mining Using SAS Enterprise Miner 231
6.1 SEMMA ...................................................................................................................... 233
6.2 Possible Tools to the EM SEMMA Design ............................................................... 235
Chapter 7: Configuration Setup of the EM Nodes 249
7.1 Enterprise Miner Input Data Source Node .................................................................. 251
7.2 Enterprise Miner Data Partition Node ........................................................................ 279
7.3 Enterprise Miner Regression Node ............................................................................. 284
7.4 Viewing the Regression Node Results ........................................................................ 306
7.5 Model Manager ........................................................................................................... 311
7.6 Enterprise Miner Neural Network Node ..................................................................... 314
7.7 An Overview of the Advanced Neural Network Node ............................................... 331
7.8 Viewing the Neural Network Results ......................................................................... 362
7.9 Neural Network Interactive Training .......................................................................... 372
7.10 Enterprise Miner Assessment Node ............................................................................ 383
7.11 Enterprise Miner SAS Code Node .............................................................................. 409
7.12 Enterprise Miner Reporter Node ................................................................................. 426
Chapter 8: Comparing Prediction Estimates 457
8.1 Comparing Multiple Linear Regression Estimates ..................................................... 462
8.2 Comparing Nonlinear Regression Estimates .............................................................. 466
8.3 Comparing Logistic Regression Estimates ................................................................. 475
8.4 Comparing Autoregressive Time Series Estimates ..................................................... 504
8.5 Comparing Discriminant Analysis Estimates ............................................................. 533
Appendix 557
References 577
Book Index 581



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