P06-1087 multiclass classification , we use pairwise voting . For all the reported experi
P03-1064 combined all their models with pairwise voting , yielding an accuracy of . The
W00-0730 class ( tag ) obtained through the pairwise voting . Since SVMs are vector based
P03-1064 generate the mapping table used in pairwise voting . The SNoW supertagger scanning
P13-2133 order of pref - erence . It is a pairwise voting , i.e. it compares every possible
J01-2002 with lexical items ) . When using pairwise voting on models trained using different
H05-1059 error reduction of supertagging by pairwise voting between left-to-right and right-toleft
P03-1064 Brill , 1995 ) . We use the same pairwise voting algorithm as in ( Chen et al.
W03-1728 Joshi , 2003 ) . In that paper , pairwise voting ( van Halteren et al. , 1998
P03-1064 of these two supertaggers with pairwise voting , we achieve an accuracy of ,
W03-1728 the opposite directions . The pairwise voting is not suitable in this application
P03-1064 Then we combine the results via pairwise voting as in ( van Halteren et al. ,
P98-1081 McNemar 's chi-square , p = 0 . 5 Pairwise Voting So far , we have only used information
E99-1025 accuracy of a classifier , and pairwise voting . Pairwise voting works as follows
P98-1081 the best combi - nation . The pairwise voting system , using all four individual
E99-1025 classifier , and pairwise voting . Pairwise voting works as follows . First , for
W03-1728 not immediately follow an MM . Pairwise voting does not use any contextual information
P10-1068 e.g. , weighted majority vote , pairwise voting ( Halteren et al. , 1998 ) ,
N01-1025 the class obtained through the pairwise voting is used as the certain score
E99-1025 vote has an accuracy of 91.93 % . Pairwise voting yields an accuracy of 92.19 %
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