Module opencv::ml

Machine Learning

The Machine Learning Library (MLL) is a set of classes and functions for statistical classification, regression, and clustering of data.

Most of the classification and regression algorithms are implemented as C++ classes. As the algorithms have different sets of features (like an ability to handle missing measurements or categorical input variables), there is a little common ground between the classes. This common ground is defined by the class cv::ml::StatModel that all the other ML classes are derived from.

See detailed overview here: @ref ml_intro.

Modules

prelude

Structs

DTrees_Node	The class represents a decision tree node.
DTrees_Split	The class represents split in a decision tree.
ParamGrid	The structure represents the logarithmic grid range of statmodel parameters.

Enums

ANN_MLP_ActivationFunctions	possible activation functions
ANN_MLP_TrainFlags	Train options
ANN_MLP_TrainingMethods	Available training methods
Boost_Types	Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.
DTrees_Flags	Predict options
EM_Types	Type of covariation matrices
ErrorTypes	%Error types
KNearest_Types	Implementations of KNearest algorithm
LogisticRegression_Methods	Training methods
LogisticRegression_RegKinds	Regularization kinds
SVMSGD_MarginType	Margin type.
SVMSGD_SvmsgdType	SVMSGD type. ASGD is often the preferable choice.
SVM_KernelTypes	%SVM kernel type
SVM_ParamTypes	%SVM params type
SVM_Types	%SVM type
SampleTypes	Sample types
StatModel_Flags	Predict options
VariableTypes	Variable types

Constants

COL_SAMPLE	each training sample occupies a column of samples
EM_DEFAULT_MAX_ITERS
EM_DEFAULT_NCLUSTERS
EM_START_AUTO_STEP
EM_START_E_STEP
EM_START_M_STEP
ROW_SAMPLE	each training sample is a row of samples
TEST_ERROR
TRAIN_ERROR
VAR_CATEGORICAL	categorical variables
VAR_NUMERICAL	same as VAR_ORDERED
VAR_ORDERED	ordered variables

Traits

ANN_MLP	Artificial Neural Networks - Multi-Layer Perceptrons.
Boost	Boosted tree classifier derived from DTrees
DTrees	The class represents a single decision tree or a collection of decision trees.
DTrees_NodeTrait	The class represents a decision tree node.
DTrees_SplitTrait	The class represents split in a decision tree.
EM	The class implements the Expectation Maximization algorithm.
KNearest	The class implements K-Nearest Neighbors model
LogisticRegression	Implements Logistic Regression classifier.
NormalBayesClassifier	Bayes classifier for normally distributed data.
ParamGridTrait	The structure represents the logarithmic grid range of statmodel parameters.
RTrees	The class implements the random forest predictor.
SVM	Support Vector Machines.
SVMSGD	! Stochastic Gradient Descent SVM classifier
SVM_Kernel
StatModel	Base class for statistical models in OpenCV ML.
TrainData	Class encapsulating training data.

Functions

create_concentric_spheres_test_set	Creates test set
rand_mv_normal	Generates sample from multivariate normal distribution

Type Definitions

ANN_MLP_ANNEAL