CRF Model
The Binary LN Model described in the bachelor thesis "Maximum Likelihood Learning And Inference In Conditional Random Fields" by Iulian Vlad Serban, University of Copenhagen, 2012.
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ConvertToNewNumberRepresentation(int number, int newRepresentationSymbolCount, int maxDigits) | CRF::CRFModel | [protected] |
CRFModel() | CRF::CRFModel | |
CRFModel(int imgWidth, int imgHeight, int MAPSTEPS) | CRF::CRFModel | |
CRFModel_GraphCut() | CRF::CRFModel_GraphCut | |
CRFModel_GraphCut(int imgWidth, int imgHeight, int MAPSTEPS) | CRF::CRFModel_GraphCut | |
EstimateLogLikelihood(const double_double_vector &Input, const int_int_vector &Output) | CRF::CRFModel | |
EstimateMAP(const double_double_vector Input) | CRF::CRFModel_GraphCut | [virtual] |
EstimateModelParameters(const std::vector< double_double_vector > &InputSamples, const std::vector< int_int_vector > &OutputSamples, size_t Steps, double StepFactor) | CRF::CRFModel | [virtual] |
EvaluateExpectedFeatureFunction(const double_double_vector &Input) | CRF::CRFModel | [protected] |
EvaluateNodeEnergy(const double_double_vector Input, const int_int_vector Output, size_t i, size_t j) | CRF::CRFModel | [protected] |
EvaluateNodeVector(const double_double_vector Input, const int_int_vector Output, size_t i, size_t j) | CRF::CRFModel | [protected] |
EvaluateOutputEnergy(const double_double_vector &Input, const int_int_vector &Output) | CRF::CRFModel | [protected] |
EvaluatePartitionFunction(const double_double_vector &Input) | CRF::CRFModel | [protected] |
imageHeight | CRF::CRFModel | [protected] |
imageWidth | CRF::CRFModel | [protected] |
MAPSteps | CRF::CRFModel | [protected] |
paramCount | CRF::CRFModel | [protected] |
statesCount | CRF::CRFModel | [protected] |
weights | CRF::CRFModel | [protected] |