Machine learning: Matlab 2015a automatic machine learning algorithm Summary

"Machine learning" Matlab 2015a self-machine learning algorithm RollupAuthor: Chen Fa St. "Introduction"Today suddenly found that the version of matlab2015a with a lot of classical machine learning methods, simple and easy to use, so write a blog in this summary (I mainly refer to the help of the MATLAB document). Matlab Each machine learning method has a lot of ways to implement, and can carry out advanced configuration (such as training decision tree set of various parameters), here due to the limitations of space, no longer detailed description. I'll just list the easiest ways to use it. Detailed use of the method, please follow the function name I gave, use in MATLAB The following command, for inspection. Doc < function name >
"Body"The functions of MATLAB for training machine learning model are mainly divided into three kinds: supervised learning without supervised learning and integrated learning
1. Supervised Learning:

class name	Method name	The name of the function	Description
Linear regression	Multivariate linear regression	Fitlm	Linear regression with multiple predictive variables
Gradual return	Stepwise	Interactive gradual regression
Multi-objective multivariate linear regression	Mvregress	Linear regression using multivariable output
Multivariate linear regression with regularization	Lasso	Multivariate linear regression using the regularization of elastic networks
Ridge	Ridge regression
Nonlinear regression	Fitnlm	Fitting Nonlinear regression model
Generalized linear model	Fitting of normal distribution	Fitglm	' Distribution ' 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 ' set to ' gamma '
Inverse Gauss Distribution Fitting	Fitglm	' Distribution ' set to ' inverse Gaussian '
A stepwise regression of variable selection	Stepwiseglm	Interactive gradual regression
Generalized linear regression with regularization	Lassoglm	Generalized linear regression using the regularization of elastic networks
Regression classification Decision Tree (CART)	Classification Tree	Fitctree	Two-fork decision tree for training classification
Regression tree	Fitrtree	Training regression two-fork decision Tree
Support Vector machine	Two classification support vector machines	Fitcsvm	Training two classification support vector machine
Multi-classification Support vector machine	Fitcecoc	Multi-class models suitable for SVM or other classifiers
discriminant analysis	Fitcdiscr	Fitting 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	The name of the function	Description
Hierarchical clustering	Clustering based on clustering tree	Cluster	Returns the category of samples after clustering
Clustering by data	Clusterdata	Returns the category of samples after clustering
into a cluster tree	Linkage	Training Hierarchical Clustering Tree
Clustering by distance	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	Searching using KD Tree
KNN Search	Knnsearch	Using Kd-tree or global K-nearest neighbor Search
Range Search	Rangesearch	Use global and Kd-tree to find adjacent to a specified range
Gaussian mixture model	Gaussian mixture model	Fitgmdist	Fitting 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	Estimating parameters of hidden Markov models 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	Calculate the most likely state path
Posterior state probability	Hmmdecode	Calculation of posterior state probability of hidden Markov model

3. Integrated Learning:

class name	Method name	The name of the function	Description
Boosting	Two categories: AdaBoostM1	Fitensemble	' method ' configured to ' AdaBoostM1 '
Two categories: Logitboost	Fitensemble	' method ' configured to ' Logitboost '
Two categories: Gentleboost	Fitensemble	' method ' configured to ' Gentleboost '
Two categories: Robustboost	Fitensemble	' method ' configured to ' Robustboost '
Multiple categories: AdaBoostM2	Fitensemble	' method ' configured to ' AdaBoostM2 '
Multiple categories: lpboosts	Fitensemble	' method ' configured to ' lpboosts '
Multiple categories: Totalboost	Fitensemble	' method ' configured to ' Totalboost '
Multiple categories: Rusboost	Fitensemble	' method ' configured to ' Rusboost '
Regression: Lsboost	Fitensemble	' method ' configured to ' LPBoost '
Elevation two classification as multiple classification model	Fitcecoc	Training multi-classification model based on two classification model
Bagging (multiple classification or regression)	Fitensemble	' method ' configured to ' Bag '
Random space (multiple classification or regression)	Fitensemble	' method ' configured to ' subspace '