[−][src]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.
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. |
Constants
Traits
| ANN_MLP | Artificial Neural Networks - Multi-Layer Perceptrons. |
| ANN_MLP_ANNEAL | 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. |
| 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. |
| RTrees | The class implements the random forest predictor. |
| SVM | Support Vector Machines. |
| SVMSGD |
|
| 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 |