Class Summary |
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 |
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