S15-2105 the final classifier , a simple weighted voting scheme was used . Each classifier was
S15-2060 annotations are combined through a weighted voting scheme inspired on the ROVER method
W03-0431 training set . An unweighted or weighted voting scheme is then typically adopted to
W12-3710 the previous step , we adopt the weighted voting scheme to determine the local position
S15-1025 annotations are combined through a weighted voting scheme inspired by the ROVER meth -
W04-0861 and they are combined using a weighted voting scheme . For the words lacking training
Q14-1025 annotator . This demonstrates that weighted voting schemes are not the correct approach
W12-0606 overall recall is to implement a weighted voting scheme among the modules , allowing
N01-1025 Matsumoto , 2000b ) 5 . By combining weighted voting schemes , we achieve accuracy of 93.91
S15-2105 , which were combined using a weighted voting scheme with weights correlated with
H89-2049 recognition system through trained , weighted voting schemes . Our sights are aimed at the
N01-1025 boosting technique is a type of weighted voting scheme , and has been applied to many
W04-0861 scheme , while W-Vot refers to the weighted voting scheme . The weights for the classifiers
H89-2049 recognizers into one through trained , weighted voting schemes . Several sets of recognizers
W03-1504 classifier by linearly combining , in a weighted voting scheme , many simple and moderately
W03-0421 classifier by linearly combining , in a weighted voting scheme , many simple and moderately
E03-1038 classifier by linearly combining , in a weighted voting scheme , many simple and moderately
W10-1308 path based measures ) , while the weighted voting scheme took more of the recommendations
E06-1016 in DI , U and DE , U. For the weighted voting schemes , DI , W and DE , W , the effect
S15-1025 results . The method involves a weighted voting scheme that had not been previously
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