W03-1024 tried to find useful features by feature elimination . Since features are not completely
P04-1016 Cancedda et al. , 2003 ) use a feature elimination method based on the size of sub-sequence
W03-1024 system 's performance . Hence , feature elimination is more reliable for reducing
N04-4002 predictive information lost during feature elimination . In order to compare the performances
W03-1024 number of features . However , feature elimination takes a long time . On the other
W12-2008 was removed . Next , a recursive feature elimination ( RFE ) based on a linear regression
W15-0709 criterion and perform recursive feature elimination by repeating the following three
W06-3401 subset selection using recursive feature elimination were carried out on the data
P08-1068 parsers as the threshold for lexical feature elimination ( see Section 3.2 ) is varied
P12-1002 work to weight-based recursive feature elimination ( RFE ) ( Lal et al. , 2006 )
W15-0709 using the method of recursive feature elimination . The resulting feature set is
W10-2801 applied : linear projections , feature elimination and random approximations . The
W03-1024 resolution . We confirmed this by feature elimination . <title> A Maximum Entropy Chinese
W06-1657 is reminiscent of the recursive feature elimination procedure first proposed in the
W13-2236 seen as a weight-based backward feature elimination variant of Obozinski et al. (
S15-2073 features , we performed backward feature elimination using the supplied training and
W12-3005 the noisiest , averaging 96.2 % feature elimination . The classifiers trained on
W14-1809 longer n-grams and a recursive feature elimination ( Kuhn and Johnson , 2013 , p.
W15-0709 feature elimination Recursive feature elimination is a greedy algorithm that relies
W10-2801 ; Baroni and Lenci , 2008 ) . Feature elimination reduces the dimensionality by
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