 | OpenCvSharp.ML Namespace |
[Missing <summary> documentation for "N:OpenCvSharp.ML"]
Classes| Class | Description | |
|---|---|---|
 | ANN_MLP |
Artificial Neural Networks - Multi-Layer Perceptrons.
|
| Boost |
Boosted tree classifier derived from DTrees
|
| DTrees |
Decision tree
|
| KNearest |
K nearest neighbors classifier
|
| LogisticRegression |
Implements Logistic Regression classifier.
|
| NormalBayesClassifier |
Bayes classifier for normally distributed data
|
| RTrees |
The class implements the random forest predictor.
|
| StatModel |
Base class for statistical models in ML
|
| SVM |
Support Vector Machines
|
| SVMKernel | |
| TrainData |
Structures| Structure | Description | |
|---|---|---|
 | DTreesNode |
The class represents a decision tree node.
|
| DTreesSplit |
The class represents split in a decision tree.
|
| ParamGrid |
The structure represents the logarithmic grid range of statmodel parameters.
|
Enumerations| Enumeration | Description | |
|---|---|---|
 | ANN_MLPActivationFunctions |
possible activation functions
|
| ANN_MLPTrainFlags |
Train options
|
| ANN_MLPTrainingMethods |
Available training methods
|
| BoostTypes |
Boosting type.
Gentle AdaBoost and Real AdaBoost are often the preferable choices.
|
| KNearestTypes |
Implementations of KNearest algorithm
|
| LogisticRegressionMethods |
Training methods
|
| LogisticRegressionRegKinds |
Regularization kinds
|
| SampleTypes |
Sample types
|
| StatModelFlags |
Predict options
|
| SVMKernelTypes |
SVM kernel type
|
| SVMParamTypes |
SVM params type
|
| SVMTypes |
SVM type
|