"MATLAB统计工具箱使用手册及联系信息合集"

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Create Clusters ................................... 11-16

k-Means Clustering

................................ 11-21

Introduction to k-Means C lustering

................... 11-21

Create Clusters and Determine Separation

............ 11-22

Determine the Correct Number of Clusters

............ 11-23

Avoid Local Minima

............................... 11-26

Gaussian Mixture Models

.......................... 11-28

Introduction to G aussian Mixture Models

.............. 11-28

Cluster with Gaussian Mixtures

..................... 11-28

Parametric Classification

Parametric Classification .......................... 12-2

Discriminant Analysis

............................. 12-3

What Is Discriminant Analysis?

...................... 12-3

Create Discriminant Analysis Classifiers

.............. 12-3

Creating a Classifier Using

ClassificationDiscriminant.fit

..................... 12-4

How the predict Method Classifies

................... 12-6

Create and Visua lize a Discriminant Analysis Classifier

.. 12-9

Improve a Discriminant Analysis Classifier

............ 12-14

Regularize a D iscriminant Analysis Classifier

.......... 12-22

Examine the Gaussian M ixture Assumption

........... 12-29

Bibliography

..................................... 12-35

Naive Bayes Classification

......................... 12-36

Supported Distributions

............................ 12-36

Performance Curves

............................... 12-39

Introduction to Pe rformance Curves

.................. 12-39

What are ROC Curves?

............................. 12-39

Evaluate Classifier Performance Using perfcurve

....... 12-39

xvi Contents

Nonparametric Supervised Learning

Supervised Learning (Machine Learning) Workflow

and Algorithms

.................................. 13-2

Steps in Supervised Learning (M achine Learning)

...... 13-2

Characteristics of Algorithms

........................ 13-7

Classification Using Nearest Neighbors

.............. 13-9

Pairwise Distance

................................. 13-9

k-Nearest Neighbor Search and Radius Search

......... 13-12

K-Nearest Neighbor Classification for Supervised

Learning

...................................... 13-25

Construct a KNN Classifier

......................... 13-25

Examine the Quality of a KNN Classifier

.............. 13-26

Predict Classification Based on a KNN Classifier

....... 13-27

Modify a KN N Classifier

............................ 13-27

Classification Trees and Regression Trees

........... 13-30

What Are Classification Tr ees and Regression Trees?

.... 13-30

Creating a Classification Tree

....................... 13-31

Creating a Reg ression Tree

......................... 13-31

Viewing a Tree

.................................... 13-32

How the Fit Methods Create Trees

................... 13-34

Predicting Responses With Classification and Regression

Trees

......................................... 13-36

Improving Classification Tree s and Regression Trees

.... 13-37

Alternative: classregtree

........................... 13-46

Ensemble Methods

................................. 13-54

Framework for Ensemble Learning

................... 13-54

Basic Ensemble Examples

.......................... 13-64

Test Ensemble Qual ity

............................. 13-66

Classification with Imbalanced Data

.................. 13-71

Classification: Imbalanced Data or Unequal

Misclassification Costs

........................... 13-76

Classification with Many Categorical Levels

........... 13-83

Surrogate Splits

.................................. 13-88

LPBoost and TotalBoost for Small Ensembles

.......... 13-93

Ensemble Regularization

........................... 13-98

Tuning RobustBoost

............................... 13-110

Random Subspace Classification

..................... 13-114

xvii

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