Uses of Class
fr.lip6.type.TrainingSample

Packages that use TrainingSample

fr.lip6.classifier
fr.lip6.kernel	Package containing generic kernels on vectors.
fr.lip6.kernel.adaptative

Uses of TrainingSample in fr.lip6.classifier

Methods in fr.lip6.classifier that return types with arguments of type TrainingSample

java.util.ArrayList<TrainingSample<T>>

SMOSVM.getTrainingSet() the ArrayList of TrainingSample used for training

Methods in fr.lip6.classifier with parameters of type TrainingSample

void	DoublePegasosSVM.train(TrainingSample<double[]> t)
void	SMOSVM.train(TrainingSample<T> t)
void	EnhancedSMOSVM.train(TrainingSample<T> t)
void	SimpleMKL.train(TrainingSample<T> train)
void	PegasosSVM.train(TrainingSample<T> t)
void	ParzenClassifier.train(TrainingSample<T> t)
void	Classifier.train(TrainingSample<T> t) Add a single example to the current training set and train the classifier

Method parameters in fr.lip6.classifier with type arguments of type TrainingSample

void	SMOSVM.setTrain(java.util.ArrayList<TrainingSample<T>> t)
void	DoublePegasosSVM.train(java.util.ArrayList<TrainingSample<double[]>> l)
void	SMOSVM.train(java.util.ArrayList<TrainingSample<T>> t)
void	EnhancedSMOSVM.train(java.util.ArrayList<TrainingSample<T>> t)
void	SimpleMKL.train(java.util.ArrayList<TrainingSample<T>> list) optimisation des poids d'après l'algorithme SimpleMKL d'Alain Rakotomamonjy
void	PegasosSVM.train(java.util.ArrayList<TrainingSample<T>> l)
void	ParzenClassifier.train(java.util.ArrayList<TrainingSample<T>> t)
void	Classifier.train(java.util.ArrayList<TrainingSample<T>> l) Replace the current training and train the classifier

Uses of TrainingSample in fr.lip6.kernel

Method parameters in fr.lip6.kernel with type arguments of type TrainingSample

double[][]	CMKernel.getKernelMatrix(java.util.ArrayList<TrainingSample<T>> e)
double[][]	ThreadedKernel.getKernelMatrix(java.util.ArrayList<TrainingSample<T>> e)
double[][]	Kernel.getKernelMatrix(java.util.ArrayList<TrainingSample<T>> e) return the Gram Matrix of this kernel computed on given samples
double[][]	Kernel.getNormalizedKernelMatrix(java.util.ArrayList<TrainingSample<T>> e) return the Gram Matrix of this kernel computed on given samples

Uses of TrainingSample in fr.lip6.kernel.adaptative

Method parameters in fr.lip6.kernel.adaptative with type arguments of type TrainingSample

double[][]	WeightedSumKernel.getKernelMatrix(java.util.ArrayList<TrainingSample<T>> e)
double[][]	ThreadedSumKernel.getKernelMatrix(java.util.ArrayList<TrainingSample<T>> e)