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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
Structs
The class represents a decision tree node.
The class represents split in a decision tree.
The structure represents the logarithmic grid range of statmodel parameters.
Enums
possible activation functions
Train options
Available training methods
Boosting type.
Gentle AdaBoost and Real AdaBoost are often the preferable choices.
Predict options
Type of covariation matrices
%Error types
Implementations of KNearest algorithm
Training methods
Regularization kinds
Margin type.
SVMSGD type.
ASGD is often the preferable choice.
%SVM kernel type
%SVM params type
%SVM type
Sample types
Predict options
Variable types
Constants
each training sample occupies a column of samples
each training sample is a row of samples
categorical variables
same as VAR_ORDERED
ordered variables
Traits
Artificial Neural Networks - Multi-Layer Perceptrons.
Boosted tree classifier derived from DTrees
The class represents a single decision tree or a collection of decision trees.
The class represents a decision tree node.
The class represents split in a decision tree.
The class implements the Expectation Maximization algorithm.
The class implements K-Nearest Neighbors model
Implements Logistic Regression classifier.
Bayes classifier for normally distributed data.
The structure represents the logarithmic grid range of statmodel parameters.
The class implements the random forest predictor.
Support Vector Machines.
!
Stochastic Gradient Descent SVM classifier
Base class for statistical models in OpenCV ML.
Class encapsulating training data.
Functions
Creates test set
Generates sample from multivariate normal distribution