|
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 |
|