| C04-1097 | a model 's distributions with | probabilistic decision trees | ( DTs ) . We build decision trees |
| P04-1074 | possible classes . Therefore , a | probabilistic decision tree | approach is preferred over conventional |
| P02-1059 | review some general features of | probabilistic decision tree | ( ProbDT , henceforth ) ( Yamanishi |
| P02-1059 | problem , we explore the use of a | probabilistic decision tree | as a ranking model . First , |
| P02-1059 | used as a test set . We build | probabilistic decision trees | of different flavors and integrate |
| P04-1074 | learning ap - proaches , namely | probabilistic decision tree | ( PDT ) and naive Bayesian classifier |
| P04-1074 | number of training cases . 3.1 | Probabilistic Decision Tree | ( PDT ) Due to two domain-specific |
| W11-0809 | TreeTagger ( Schmid , 1994 ) based on | probabilistic decision trees | , as well as TnT ( Brants , 2000 |
| P02-1059 | ticular , we explore the use of | probabilistic decision tree | within the clustering framework |
| P04-1074 | we train two clas - sifiers , a | Probabilistic Decision Tree | Classifier ( PDT ) and a Naïve |
| P99-1049 | probability is estimated by a | probabilistic decision tree | and we have P ( B „ , , |
| W97-0105 | of unrestricted English text . | Probabilistic decision trees | are utilized as a means of prediction |
| W97-0105 | of unrestricted English text . | Probabilistic decision trees | are utilized as a mea.ns of prediction |
| P02-1059 | tasks . 2 Supervised Ranking with | Probabilistic Decision Tree | One technical problem associated |
