OpenCV
3.3.0
Open Source Computer Vision
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Classes | |
class | cv::ml::ANN_MLP |
Artificial Neural Networks - Multi-Layer Perceptrons. More... | |
class | cv::ml::Boost |
Boosted tree classifier derived from DTrees. More... | |
class | cv::ml::DTrees |
The class represents a single decision tree or a collection of decision trees. More... | |
class | cv::ml::EM |
The class implements the Expectation Maximization algorithm. More... | |
class | cv::ml::KNearest |
The class implements K-Nearest Neighbors model. More... | |
class | cv::ml::LogisticRegression |
Implements Logistic Regression classifier. More... | |
class | cv::ml::NormalBayesClassifier |
Bayes classifier for normally distributed data. More... | |
class | cv::ml::ParamGrid |
The structure represents the logarithmic grid range of statmodel parameters. More... | |
class | cv::ml::RTrees |
The class implements the random forest predictor. More... | |
class | cv::ml::StatModel |
Base class for statistical models in OpenCV ML. More... | |
class | cv::ml::SVM |
Support Vector Machines. More... | |
class | cv::ml::SVMSGD |
Stochastic Gradient Descent SVM classifier. More... | |
class | cv::ml::TrainData |
Class encapsulating training data. More... | |
Enumerations | |
enum | cv::ml::ErrorTypes { cv::ml::TEST_ERROR = 0, cv::ml::TRAIN_ERROR = 1 } |
Error types More... | |
enum | cv::ml::SampleTypes { cv::ml::ROW_SAMPLE = 0, cv::ml::COL_SAMPLE = 1 } |
Sample types. More... | |
enum | cv::ml::VariableTypes { cv::ml::VAR_NUMERICAL =0, cv::ml::VAR_ORDERED =0, cv::ml::VAR_CATEGORICAL =1 } |
Variable types. More... | |
Functions | |
void | cv::ml::createConcentricSpheresTestSet (int nsamples, int nfeatures, int nclasses, OutputArray samples, OutputArray responses) |
Creates test set. More... | |
void | cv::ml::randMVNormal (InputArray mean, InputArray cov, int nsamples, OutputArray samples) |
Generates sample from multivariate normal distribution. More... | |
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.
enum cv::ml::ErrorTypes |
enum cv::ml::SampleTypes |
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.
mean | an average row vector |
cov | symmetric covariation matrix |
nsamples | returned samples count |
samples | returned samples array |