| A00-1024 | identifier , we make use of a | decision tree | to capture the characteristics |
| A00-1024 | the cases where the misspelling | decision tree | failed to identify a misspelling |
| A00-1024 | Indurkhya , 1998 ) . Further - more , | decision trees | are well-suited for combining |
| A00-1024 | name identifier . We utilize a | decision tree | to model the characteristics |
| A00-1024 | errors . Both components use a | decision tree | architecture to combine multiple |
| A00-1024 | errors . Each component uses a | decision tree | architecture to combine multiple |
| A00-1024 | confidence measure is accepted . The | decision trees | return a confidence measure for |
| A00-1024 | should be obtained by using other | decision tree | software . Indeed , the results |
| A00-1024 | features that we use to train the | decision tree | are intended to capture the characteristics |
| A00-1024 | given in Table 4 . Again , the | decision tree | approach is a significant improvement |
| A00-1024 | Baluja and his colleagues use a | decision tree | classifier to identify proper |
| A00-1024 | this project , we made use of the | decision tree | that is part of IBM 's Intelligent |
| A00-1024 | consider the results of training a | decision tree | to identify misspellings using |
| A00-1024 | not surprising since the name | decision tree | has higher results and hence |
| A00-1024 | productive features to include in the | decision tree | . Research that is more similar |
| A00-1024 | training and test data for the | decision tree | consists of 7000 cases of unknown |
| A00-1022 | ID3 , C4 .5 and C5 .0 produce | decision trees | , RIPPER is a rulebased learner |
| A00-1022 | during the learning phase , e.g. | decision trees | , decision rules or probability |
| A00-1024 | the information provided to the | decision tree | training algorithm . For many |
| A00-1013 | These rules were integrated into a | decision tree | structure , as illustrated in |
