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