Class SVM
- Namespace
- OpenCvSharp.ML
- Assembly
- OpenCvSharp.dll
Support Vector Machines
public class SVM : StatModel, IDisposable- Inheritance
-
SVM
- Implements
- Inherited Members
Properties
- C
Parameter C of a %SVM optimization problem. For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0.
- ClassWeights
Optional weights in the SVM::C_SVC problem, assigned to particular classes.
- Coef0
Parameter coef0 of a kernel function. For SVM::POLY or SVM::SIGMOID. Default value is 0.
- Degree
Parameter degree of a kernel function. For SVM::POLY. Default value is 0.
- Gamma
Parameter gamma of a kernel function. For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1.
- KernelType
Type of a %SVM kernel. See SVM::KernelTypes. Default value is SVM::RBF.
- Nu
Parameter nu of a %SVM optimization problem. For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0.
- P
Parameter epsilon of a %SVM optimization problem. For SVM::EPS_SVR. Default value is 0.
- TermCriteria
Termination criteria of the iterative SVM training procedure which solves a partial case of constrained quadratic optimization problem.
- Type
Type of a %SVM formulation. Default value is SVM::C_SVC.
Methods
- Create()
Creates empty model. Use StatModel::Train to train the model. Since %SVM has several parameters, you may want to find the best parameters for your problem, it can be done with SVM::TrainAuto.
- GetDecisionFunction(int, OutputArray, OutputArray)
Retrieves the decision function
- GetDefaultGrid(ParamTypes)
Generates a grid for SVM parameters.
- GetSupportVectors()
Retrieves all the support vectors
- Load(string)
Loads and creates a serialized svm from a file. Use SVM::save to serialize and store an SVM to disk. Load the SVM from this file again, by calling this function with the path to the file.
- LoadFromString(string)
Loads algorithm from a String.
- TrainAuto(TrainData, int, ParamGrid?, ParamGrid?, ParamGrid?, ParamGrid?, ParamGrid?, ParamGrid?, bool)
Trains an %SVM with optimal parameters.