| J89-4002 | expert system is in fact just a | binary decision tree | . At each internal node there |
| H90-1067 | starting from the root of the | binary decision tree | . The decision function associated |
| W01-0502 | straight-forward . Theorem 3 Let be a | binary decision tree | with internal nodes . Then , |
| H90-1067 | et al. systematically consider | binary decision trees | applied to various classification |
| W10-1763 | between si and tj . A separate | binary decision tree | is grown for each source word |
| H94-1015 | which automatically generates a | binary decision tree | from training data . Although |
| J95-4004 | set of primitive queries , any | binary decision tree | can be converted into a transformation |
| H90-1067 | extensively addressed in the theory of | binary decision trees | \ -LSB- 5 , 8 , 2 \ -RSB- . For |
| P02-1059 | for each class ck . Consider a | binary decision tree | in Fig 1 . Let X1 and X2 represent |
| W01-1616 | in ( Allen and Core , 1997 ) , | binary decision trees | were designed to guide the classification |
| P98-2233 | automatic classifier that produces a | binary decision tree | . Although it may be necessary |
| W01-0502 | SM . Extending the proof beyond | binary decision trees | is straight-forward . Theorem |
| W04-0825 | . It can be defined as simple | binary decision trees | . Training data are used in order |
| S01-1016 | contexts . Moreover , SCT , which are | binary decision trees | , permit a simple interpretation |
| J96-4003 | phonological features , we obtained | binary decision trees | ( although we could just as easily |
| H90-1067 | encoding . Our MMI encoders are | binary decision trees | built to maximize the average |
| P02-1049 | response variables . CART trees are | binary decision trees | . A CLASSIFICATION tree specifies |
| J94-1002 | Hirschberg ( 1992 ) have recently used | binary decision trees | to predict the presence or absence |
| J94-1002 | specifically for classification . A | binary decision tree | was trained using the baseline |
| H90-1067 | impurity " criteria ) for the | binary decision trees | include the average leaf-node-conditional |
