Lines Matching defs:train
133 of this class into StatModel::train.
154 /** @brief Returns matrix of train samples
318 CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
326 CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
351 /** @brief Create and train model with default parameters
355 template<typename _Tp> static Ptr<_Tp> train(const Ptr<TrainData>& data, int flags=0)
358 return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
385 Use StatModel::train to train the model after creation. */
466 The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
636 subset is used to test the model, the others form the train set. So, the %SVM algorithm is
646 to such proportion in the whole train dataset.
709 Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
900 The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
901 can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
912 The current public interface of the class allows user to train only a single decision tree, however
993 If all absolute differences between an estimated value in a node and values of train samples
1088 trained using train method (see StatModel::train). Alternatively, you can load the model from
1142 Use StatModel::train to train the model, StatModel::train to create and train the model,
1193 Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
1240 Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
1357 /** Train options */
1378 Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
1379 Note that the train method has optional flags: ANN_MLP::TrainFlags.
