Index of TMVA classes

• TMVA
• TMVA::BinarySearchTreeBinary search tree including volume search method
• TMVA::BinarySearchTreeNodeNode for the BinarySearchTree
• TMVA::BinaryTreeBase class for BinarySearch and Decision Trees
• TMVA::CrossEntropyImplementation of the CrossEntropy as separation criterion
• TMVA::DecisionTreeImplementation of a Decision Tree
• TMVA::DecisionTreeNodeNode for the Decision Tree
• TMVA::FactoryThe factory creates all MVA methods, and performs their training and testing
• TMVA::GeneticANNANN interface class for genetic algorithm
• TMVA::GeneticBaseBase definition for genetic algorithm
• TMVA::GeneticCutsCut optimisation interface class for genetic algorithm
• TMVA::GeneticGenesGenes definition for genetic algorithm
• TMVA::GeneticPopulationPopulation definition for genetic algorithm
• TMVA::GeneticRangeRange definition for genetic algorithm
• TMVA::GiniIndexImplementation of the GiniIndex as separation criterion
• TMVA::IMethodMethod Interface
• TMVA::MethodANNBaseBase class for TMVA ANNs
• TMVA::MethodBDTAnalysis of Boosted Decision Trees
• TMVA::MethodBaseVirtual base class for all TMVA method
• TMVA::MethodBayesClassifierFriedman's BayesClassifier method
• TMVA::MethodCFMlpANNInterface for Clermond-Ferrand artificial neural network
• TMVA::MethodCFMlpANN_UtilsImplementation of Clermond-Ferrand artificial neural network
• TMVA::MethodCommitteeAnalysis of Boosted MVA methods
• TMVA::MethodCutsMultivariate optimisation of signal efficiency
• TMVA::MethodFisherAnalysis of Fisher discriminant (Fisher or Mahalanobis approach)
• TMVA::MethodHMatrixH-Matrix method, a simple comparison of chi-squared estimators for signal and background
• TMVA::MethodLikelihoodLikelihood analysis ("non-parametric approach")
• TMVA::MethodMLPMulti-layer perceptron implemented specifically for TMVA
• TMVA::MethodPDERSMulti-dimensional probability density estimator range search (PDERS) method
• TMVA::MethodRuleFitFriedman's RuleFit method
• TMVA::MethodSVMFriedman's SVM method
• TMVA::MethodTMlpANNImplementation of interface for TMultiLayerPerceptron
• TMVA::MethodVariableWrapper class for a single variable "MVA"
• TMVA::MisClassificationErrorImplementation of the MisClassificationError as separation criterion
• TMVA::MsgLoggerostringstream derivative to redirect and format logging output
• TMVA::NodeNode for the BinarySearch or Decision Trees
• TMVA::PDFPDF wrapper for histograms
• TMVA::Rankingmethod-specific ranking for input variables
• TMVA::ReaderInterpret the trained MVAs in an analysis context
• TMVA::RootFinderRoot finding using Brents algorithm
• TMVA::RuleFitthe actual calculations to Friedman's RuleFit method
• TMVA::SdivSqrtSplusBImplementation of the SdivSqrtSplusB as separation criterion
• TMVA::SeparationBaseInterface to different separation critiera used in training algorithms
• TMVA::SimulatedAnnealingBaseBase class for Simulated Annealing fitting
• TMVA::SimulatedAnnealingCutsConcrete Simulated Annealing fitter for cut optimisation
• TMVA::TActivationChooserClass for choosing activation functions
• TMVA::TActivationIdentityIdentity activation function for TNeuron
• TMVA::TActivationRadialRadial basis activation function for TNeuron
• TMVA::TActivationSigmoidSigmoid activation function for TNeuron
• TMVA::TActivationTanhTanh sigmoid activation function for TNeuron
• TMVA::TNeuronNeuron class used by MethodANNBase derivative ANNs
• TMVA::TNeuronInputAbsCalculates the sum of the absolute values of the weighted inputs
• TMVA::TNeuronInputChooserClass for choosing neuron input functions
• TMVA::TNeuronInputSqSumCalculates square of weighted sum of neuron inputs
• TMVA::TNeuronInputSumCalculates weighted sum of neuron inputs
• TMVA::TSpline1Linear interpolation class
• TMVA::TSpline2Quadratic interpolation class (using quadrax)
• TMVA::TSynapseSynapse class used by MethodANNBase and derivatives
• TMVA::TimerTiming information for training and evaluation of MVA methods
• TMVA::Tools