| W00-0710 | ensemble of separate classifiers ( | dichotomizers | ) must be trained to learn the |
| W00-0710 | size and composition for every | dichotomizer | . 5 Results Table 3 lists the |
| W00-0710 | of binary-valued classifiers ( | dichotomizers | ) . In this study , the idea |
| W00-0710 | bit predictions of the various | dichotomizers | are combined to produce a codeword |
| W00-0710 | selection approach , where every | dichotomizer | is trained to select ( pos - |
| W00-0710 | the number of errors made by the | dichotomizer | ( Cesa-Bianchi et al. , 1996 |
| W00-0710 | 10-fold cross-validation . For | dichotomizer | weighting , error information |
| W00-0710 | Selecting different features per | dichotomizer | was proposed for this purpose |
| W00-0710 | classifier . 2 Dichotomizer Ensembles | Dichotomizer | ensembles must be diverse apart |
| W00-0710 | predicted by a voting committee of | dichotomizers | , each of which randomly selects |
| W00-0710 | values of k larger than 1 , and | dichotomizer | weight - ing . The following |
| W00-0710 | variance of the classifier . 2 | Dichotomizer | Ensembles Dichotomizer ensembles |
