Searched refs:ensemble (Results 1 – 9 of 9) sorted by relevance
84 ensemble = 0; in CvBoostTree()98 ensemble = 0; in clear()107 ensemble = _ensemble; in train()170 double* weak_eval = ensemble->get_weak_response()->data.db; in try_split_node()185 const double* weights = ensemble->get_subtree_weights()->data.db; in calc_node_dir()249 const double* weights = ensemble->get_subtree_weights()->data.db; in find_split_ord_class()256 int boost_type = ensemble->get_params().boost_type; in find_split_ord_class()257 int split_criteria = ensemble->get_params().split_criteria; in find_split_ord_class()341 const double* weights = ensemble->get_subtree_weights()->data.db; in CV_IMPLEMENT_QSORT_EX()347 int boost_type = ensemble->get_params().boost_type; in CV_IMPLEMENT_QSORT_EX()[all …]
84 ensemble = 0; in CvBoostTree()98 ensemble = 0; in clear()107 ensemble = _ensemble; in train()169 double* weak_eval = ensemble->get_weak_response()->data.db; in try_split_node()185 const double* weights = ensemble->get_subtree_weights()->data.db; in calc_node_dir()256 const double* weights = ensemble->get_subtree_weights()->data.db; in find_split_ord_class()276 int boost_type = ensemble->get_params().boost_type; in find_split_ord_class()277 int split_criteria = ensemble->get_params().split_criteria; in find_split_ord_class()382 const double* weights = ensemble->get_subtree_weights()->data.db; in find_split_cat_class()388 int boost_type = ensemble->get_params().boost_type; in find_split_cat_class()[all …]
1067 const CvMat* subsample_idx, CvBoost* ensemble );1071 CvBoost* ensemble, CvDTreeTrainData* _data );1103 CvBoost* ensemble; member in CvBoostTree
986 int size = (int)pow( 2.f, (float)ensemble->get_params().max_depth); in write()1054 ensemble = _ensemble; in read()
1040 const CvMat* subsample_idx, CvBoost* ensemble );1044 CvBoost* ensemble, CvDTreeTrainData* _data );1072 CvBoost* ensemble; variable
229 the ensemble is increased, a larger number of the training samples are classified correctly and with251 classification and regression problems. Random trees is a collection (ensemble) of tree predictors
1216 msgstr "Impossible d'obtenir un ensemble vide de signaux\n"2089 "l'ensemble du système de fichiers au prochain démarrage. Le réétiquetage "2116 "L'activation de SELinux va provoquer un réétiquetage de l'ensemble du "2238 "Choisissez si vous souhaitez réétiqueter l'ensemble du système de fichiers "3893 "communiquer ensemble"
1460 ADV "ensemble" "~as'~abl(@)"2959 N_M_SG "ensemble" "~as'~abl(@)Þ"
2725 N "ensemble" "An-s'Am-b@l"