| A00-2020 | weighted sum uses a pseudo-count | predictor | . This predictor computes the |
| A00-2017 | experiment uses MLE , the majority | predictor | . In addition , we conducted |
| A00-2017 | to the performance of the word | predictor | . Finally , we describe a large |
| A00-2017 | The application in which a word | predictor | is used might give a partial |
| A00-2003 | by a linear combination of the | predictor | variables . Variable weights |
| A00-2020 | every context . In fact , these | predictors | can be any probability distribution |
| A00-2028 | Automatic Problematic Dialogue | Predictor | Our experiments apply the machine |
| A00-1021 | using a linear function of seven | predictor | variables . We provide an evaluation |
| A00-2017 | POS tags in order to derive the | predictor | . In this paper we show that |
| A00-2029 | looks at prosody as one possible | predictor | of ASR performance . ASR performance |
| A00-2005 | the value predicted by the most | predictors | , the majority vote . 2.2 Bagging |
| A00-2020 | pseudo-count predictor . This | predictor | computes the probability of an |
| A00-2028 | generalize our problematic dialogue | predictor | to other systems . Thus we also |
| A00-2028 | automatic problematic dialogue | predictor | ( Cohen , 1996 ) . Section 4 |
| A00-2028 | likely to produce generalized | predictors | ( Litman et al. , 1999 ) . The |
| A88-1002 | words and phrases that were good | predictors | of a particular topic but occurred |
| A00-2005 | training set . Algorithm : Bagging | Predictors | ( Breiman , 1996 ) ( 1 ) Given |
| A00-2028 | input features that are used as | predictors | for the classes . We start with |
| A00-2017 | to be used when evaluating the | predictors | . Tables 4,5 present the results |
| A00-1031 | - ing . The suffix is a strong | predictor | for word classes , e.g. , words |
