D08-1034 development set . The code of feature selection algorithm is designed in Figure 3 . 1 .
C86-1134 background of the scene ? In NAOS the selection algorithm answering the above question
C02-1079 of merit for the best analysis selection algorithm . First , the acquisition of
D10-1024 Contribution ( TC ) , a feature selection algorithm developed for document clustering
D08-1034 , we designed a simple feature selection algorithm to calibrate features for each
D10-1061 baseline ( seed ) SMT system . The selection algorithm is then trained to choose , from
D10-1021 second technique is a new feature selection algorithm which uses an ensemble of feature
A97-1032 the human judge . ( Since the selection algorithm produces only a single tiling
C86-1134 reaL-wnrld events ( visual data ) . The selection algorithms are based on low-level , verbinherent
C02-1079 addition to verbs , the best analysis selection algorithms could also take advantage of
C02-1079 The methods of the best analysis selection algorithm described in this paper show
D10-1044 implemented a very simple sentence selection algorithm in which parallel sentence pairs
D10-1024 provided that a reasonable feature selection algorithm is employed . The LDA topic model
D09-1004 system . 2 ) Using a greedy feature selection algorithm , a large-scale feature engineering
C86-1134 drives . According to the deep ease selection algorithm a DIRECTION and LOCATIVE should
D08-1034 optimal solution . The feature selection algorithm is as follows . Each time we
D10-1061 development set D for training the selection algorithm and for tuning the SMT . •
C92-4189 fiiml modification to the sense selection algorithm mcleased pelfolmalice by 10 %
D10-1070 , we adopt the greedy feature selection algorithm as described in Jiang and Ng
D09-1133 . We adopt the greedy feature selection algorithm as described in Jiang and Ng
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