"MATLAB统计工具箱使用手册及联系信息合集"

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The "MATLAB统计工具箱使用手册.pdf" is a comprehensive guide to using the Statistics Toolbox in MATLAB. The toolbox provides a wide range of functions for statistical analysis, data visualization, and machine learning. This user's guide, targeted at users of R2012b, provides detailed information on how to use the toolbox effectively. The guide covers various aspects of the Statistics Toolbox, including data preprocessing, hypothesis testing, regression analysis, and multivariate analysis. It also includes information on how to create and customize plots for data visualization. Additionally, the guide provides examples and code snippets to illustrate the usage of different functions within the toolbox. In addition to the technical content, the guide also includes information on how to contact MathWorks for technical support, product enhancement suggestions, bug reports, documentation errors, order status, license renewals, and general inquiries. Contact information for MathWorks is provided, including email addresses, phone numbers, and postal addresses. Overall, the "MATLAB统计工具箱使用手册.pdf" is a valuable resource for users of the Statistics Toolbox in MATLAB. It provides the necessary information for users to effectively utilize the toolbox for their statistical analysis and data visualization needs. Additionally, it offers avenues for users to seek assistance and provide feedback to MathWorks, ensuring that the toolbox continues to meet the evolving needs of its users.

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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