| A97-1051 | learning procedure explores . The | learner | uses indexing based on the actual |
| A97-1051 | initial corpus and then invokes the | learner | to derive a set of pre-tagging |
| A00-2005 | parsing system as the embedded | learner | . The formulation is given in |
| C00-1048 | tbr the " transtbrmation rule | learner | " ( Brill , 1995 ) . This learning |
| A00-2005 | possible outputs of the underlying | learner | . Boosting has been used in a |
| A00-2005 | samples that it is given . The | learner | must be able to get more correct |
| A00-2017 | In particular , we provide the | learner | with a rich set of features that |
| A00-1032 | entry words in Oxford Advanced | Learner | 's Dictio - nary ( Mitton , 1992 |
| A00-1022 | trees , RIPPER is a rulebased | learner | and the Naive Bayes algorithm |
| A00-1022 | Eager Learning : This type of | learners | constructs a representation for |
| A00-1022 | Vapnik , 1995 ) . SVMs are binary | learners | in that they distinguish positive |
| A00-2007 | grammar with rules induced by a | learner | that was based upon the maximum |
| A00-1022 | examples for each class . Like eager | learners | , they construct a representation |
| C00-1022 | kNN ) , mid then ( ; lie TiMBL | learner | ( Daelemans et al. , 1999 ) , |
| A00-2009 | and finds that a decision tree | learner | ( 78 % ) and a Naive Bayesian |
| A00-2005 | how well boosting worked with a | learner | that better satisfied the weak |
| A97-1051 | during each learning cycle , the | learner | tries out applicable rule instances |
| C00-1035 | and presented to a memory based | learner | , such as TiMBL ( Daelemans et |
| A97-1051 | interpreter , the phrase rule | learner | , and a number of discourse-level |
| A00-2013 | general features are added ) , the | learner | is now allowed to vary the weights |
