| Class | Description |
|---|---|
| CvANN_MLP |
MLP model.
|
| CvANN_MLP_TrainParams |
Parameters of the MLP training algorithm.
|
| CvBoost |
Boosted tree classifier derived from "CvStatModel".
|
| CvBoostParams |
Boosting training parameters.
|
| CvDTree |
The class implements a decision tree as described in the beginning of this
section.
|
| CvDTreeParams |
The structure contains all the decision tree training parameters.
|
| CvERTrees |
The class implements the Extremely randomized trees algorithm.
|
| CvGBTrees |
The class implements the Gradient boosted tree model as described in the
beginning of this section.
|
| CvGBTreesParams |
GBT training parameters.
|
| CvKNearest |
The class implements K-Nearest Neighbors model as described in the beginning
of this section.
|
| CvNormalBayesClassifier |
Bayes classifier for normally distributed data.
|
| CvParamGrid |
The structure represents the logarithmic grid range of statmodel parameters.
|
| CvRTParams |
Training parameters of random trees.
|
| CvRTrees |
The class implements the random forest predictor as described in the
beginning of this section.
|
| CvStatModel |
Base class for statistical models in ML.
|
| CvSVM |
Support Vector Machines.
|
| CvSVMParams |
SVM training parameters.
|
| EM |
The class implements the EM algorithm as described in the beginning of this
section.
|
| Ml |