"Machine learning" Matlab 2015a self-bringing machine learning algorithm summary

MATLAB machine learning did not see what tutorial, only a series of functions, had to record:

Matlab Each machine learning method is implemented in many ways, and can be advanced configuration (such as the training decision tree when the various parameters set), here due to space limitations, no longer described in detail. I'll just list the simplest ways to use it. For detailed use, please follow the function name I gave you in MATLAB using The following command, to be consulted. Doc < function name > the Body "The functions of MATLAB for training machine learning models are divided into three main categories:

1. Supervised learning
2. Unsupervised learning
3. Integrated learning

1. Supervised Learning:

Class name	Method name	Name of function	Description
Linear regression	Multivariate linear regression	Fitlm	Linear regression with multiple predictor variables
	Stepwise regression	Stepwise	Interactive stepwise regression
	Multivariate linear regression of multiple targets	Mvregress	Linear regression with multivariable output
	Multivariate linear regression with regularization	Lasso	Multivariate linear regression using elastic mesh regularization
		Ridge	Ridge regression
Nonlinear regression		Fitnlm	Fitting Nonlinear regression model
Generalized linear model	Normal distribution fitting	Fitglm	' Distribution ' is set to ' normal '
	Two-Item distribution fitting	Fitglm	' Distribution ' set to ' binomial '
	Poisson distribution Fitting	Fitglm	' Distribution ' set to ' Poisson '
	Gamma Distribution Fitting	Fitglm	' Distribution ' is set to ' gamma '
	Inverse Gaussian distribution fitting	Fitglm	' Distribution ' set to ' inverse Gaussian '
	stepwise regression for variable selection	Stepwiseglm	Interactive stepwise regression
	Generalized linear regression with regularization	Lassoglm	Generalized linear regression using elastic mesh regularization
Regression classification Decision Tree (CART)	Classification Tree	Fitctree	Training classification Binary Decision tree
	Regression tree	Fitrtree	Training regression Binary Decision tree
Support Vector machine	Two-class support vector machine	Fitcsvm	Training two classification support vector machine classification
	Multi-classification Support vector machine	Fitcecoc	Multi-class models for SVM or other classifiers
discriminant analysis		Fitcdiscr	Fit discriminant Analysis Classifier
Naive Bayesian classifier		Fitcnb	Training naive Bayesian classification
Nearest neighbor	K-Nearest Neighbor	Fitcknn	Fitting K-Nearest neighbor Classifier

2. Unsupervised Learning:

Class name	Method name	Name of function	Description
Hierarchical clustering	Clustering with clustering trees	Cluster	Returns the cluster of sample categories
	Clustering with Data	Clusterdata	Returns the cluster of sample categories
	into a cluster tree	Linkage	Training Hierarchical Clustering Tree
By distance Clustering	K-means Clustering	Kmeans
	K-medoids Clustering	Kmedoids
Nearest neighbor	Global nearest Neighbor Search	Exhaustivesearcher	Prepare global Nearest Neighbor Search
	KD Tree Search	Kdtreesearcher	Generate KD Tree
		Createns	Using KD Tree Search
	KNN Search	Knnsearch	Using Kd-tree or global K-nearest neighbor Search
	Range Search	Rangesearch	Use global and Kd-tree to find the nearest neighbor of a specified range
Gaussian mixture model	Gaussian mixture model	Fitgmdist	Fit Gaussian mixture model
	Clustering based on Gaussian mixture model	Cluster	Generation of clustering based on Gaussian mixture model
Hidden Markov model	Estimation of Hidden Markov models	Hmmtrain	Estimation of hidden Markov model parameters by observation
		Hmmestimate	Estimating parameters by state and observation
	Generating observation sequences	Hmmgenerate	Generation of Hidden Markov model States and observations
	Most likely state path	Hmmviterbi	To calculate the most probable state path
	Posterior state probabilities	Hmmdecode	Calculation of posterior state probabilities of hidden Markov models

3. Integrated Learning:

Class name	Method name	Name of function	Description
Boosting	Category II: AdaBoostM1	Fitensemble	' Method ' configured as ' AdaBoostM1 '
	Category II: Logitboost	Fitensemble	' Method ' configured as ' Logitboost '
	Category II: Gentleboost	Fitensemble	' Method ' configured as ' Gentleboost '
	Category II: Robustboost	Fitensemble	' Method ' configured as ' Robustboost '
	Multi-Category: AdaBoostM2	Fitensemble	' Method ' configured as ' AdaBoostM2 '
	Multi-Category: Lpboosts	Fitensemble	' Method ' configured as ' lpboosts '
	Multi-Category: Totalboost	Fitensemble	' Method ' configured as ' Totalboost '
	Multi-Category: Rusboost	Fitensemble	' Method ' configured as ' Rusboost '
	Regression: Lsboost	Fitensemble	' Method ' configured as ' LPBoost '
	Promotion two classification as a multi-classification model	Fitcecoc	Training multi-classification model based on two classification model
Bagging (Multi-classification or regression)		Fitensemble	' Method ' configured as ' Bag '
Random Space (multi-classification or regression)		Fitensemble	' Method ' configured as ' subspace '

"Machine learning" Matlab 2015a self-bringing machine learning algorithm summary