MACHINELEARNINGwithSASENTERPRISEMINER - (EPUB全文下载)

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MACHINE LEARNING WITH SAS ENTERPRISE MINER
 
 
 
 
 
C. Perez
 
ÍNDICE
introducTION
SAS ENTERPRISE MINER ENVIRONMENT FOR MACHINE LEARNING
1.1 SAS Enterprise MINER Introduction
1.2 Starting with SAS Enterprise Miner
1.2.1 Create a New Project
1.2.2 Project Start Code
1.2.3 Create a New Process Flow Diagram
1.2.4 Create a Data Source
1.2.5 Connect Nodes in Diagram Workspace
1.2.6 Run the Process Flow Diagram
1.2.7 View Results
1.2.8 Create A Model Package
1.3 SAS Enterprise Miner User Interface
1.3.1 Sas Enterprise Miner main menu
1.3.2 The SAS Enterprise Miner Node Toolbar
SUPERVISED LEARNING. Predictive MODELING with sas enterprise miner
2.1 modeling PREDICTIVE Techniques with SAS ENTERPRISE  MINER
2.2 Regression node:  multiple regression model
2.2.1 Regression Node Data Set Requirements
2.2.2 Regression Node Train Properties: Equation
2.2.3 Regression Node Train Properties: Class Targets
2.2.4 Regression Node Train Properties: Model Options
2.2.5 Regression Node Train Properties: Model Selection
2.2.6 Example 1. Regression
2.2.7 Example 2. Logistic Regression
2.3 Dmine Regression Node
2.3.1 Dmine Regression Node Data Set Requirements
2.4 Partial Least Squares Node
2.4.1 Partial Least Squares Node Algorithm
2.4.2 Partial Least Squares Node Train Properties: Modeling Techniques
2.4.3 Partial Least Squares Node Example
2.5 LARS Node
2.5.1 LARs Node Example
Create the Data Source
Place the Nodes on the Diagram Workspace
Configure the LARs Node
Run the LARs Node
Open the LARs Node Results Window
Examine the Results
SUPERVISED LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES. DECISION TREES WITH SAS ENTERPRISE MINER
3.1 DECISION TREE NODE
3.1.1 Decision Tree Node Variable Requirements
3.1.2 Results Tables and Plots
3.1.3 Tree Properties Window
3.1.4 Decision Tree Interactive Training
3.1.5 Decision Tree Node Output Data Sources
3.1.6 Example
SUPERVISED LEARNING. NEURAL NETWORKS IN sas enterprise miner
4.1 NEURAL network Description
4.2 NEURAL Networks with SAS ENTERPRISE     MINER
4.3
Optimization and adjustment of models with nets: Neural Network node
4.3.1 Overview of Feedforward Neural Networks
4.3.2 Simple Neural Networks
4.3.3 Perceptrons
4.3.4 Hidden Layers
4.3.5 Multilayer Perceptrons (MLPs)
4.3.6 Radial Basis Function (RBF) Networks
4.3.7 Local Processing Networks
4.3.8 Width and Altitude
4.3.9 Ordinary RBF and Normalized RBF
4.3.10 Error Functions
4.3.11 Initialization
4.3.12 Preliminary Training
4.3.13 Training Techniques
4.3.14 Scoring
4.3.15 Preparin ............

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