Machine Learning

Overview

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

// enums

enum cv::ml::ErrorTypes;
enum cv::ml::SampleTypes;
enum cv::ml::VariableTypes;

// classes

class cv::ml::ANN_MLP;
class cv::ml::Boost;
class cv::ml::DTrees;
class cv::ml::EM;
class cv::ml::KNearest;
class cv::ml::LogisticRegression;
class cv::ml::NormalBayesClassifier;
class cv::ml::ParamGrid;
class cv::ml::RTrees;
class cv::ml::SVM;
class cv::ml::SVMSGD;
class cv::ml::StatModel;
class cv::ml::TrainData;

// global functions

void
cv::ml::createConcentricSpheresTestSet(
    int nsamples,
    int nfeatures,
    int nclasses,
    OutputArray samples,
    OutputArray responses
    );

void
cv::ml::randMVNormal(
    InputArray mean,
    InputArray cov,
    int nsamples,
    OutputArray samples
    );

Detailed Documentation

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: Machine Learning Overview.

Global Functions

void
cv::ml::createConcentricSpheresTestSet(
    int nsamples,
    int nfeatures,
    int nclasses,
    OutputArray samples,
    OutputArray responses
    )

Creates test set.

void
cv::ml::randMVNormal(
    InputArray mean,
    InputArray cov,
    int nsamples,
    OutputArray samples
    )

Generates sample from multivariate normal distribution.

Parameters:

mean	an average row vector
cov	symmetric covariation matrix
nsamples	returned samples count
samples	returned samples array