W15-3807 research is to explore the use of stacked generalization learning for the medical concept
W15-3807 train a meta-classifier using stacked generalization with a feature set generated
W14-3409 a cross-validation version of stacked generalization designed to mitigate against
S15-2118 combine several base systems using stacked generalization ( Wolpert , 1992 ) . An initial
S13-1030 feature stacking ( Lui , 2012 ) and stacked generalization ( Wolpert , 1992 ) . A general
J98-1005 . It is based upon Wolpert 's stacked generalization ( Wolpert 1992 ) . In this technique
D13-1041 linking method based on stacking . Stacked generalization ( Wolpert , 1992 ) is a powerful
W14-3409 alternative approach , often called stacked generalization ( Wolpert , 1992 ) , would be
W01-0506 combining classifiers , known as stacked generalization , in the context of anti-spam
W07-1014 using our reimplementation of the stacked generalization proposed by Ting and Witten .
H05-1009 combine classifiers that is based on stacked generalization ( Wolpert , 1992 ) , i.e. , learning
W01-0506 presents an empirical evaluation of stacked generalization , a scheme for combining automatically
W15-3807 information . Our research explores stacked generalization as a metalearning technique to
S13-1024 alternatives , we exploit the stacked generalization ( STACKING ) algorithm ( Wolpert
W01-0506 Conclusion In this paper we adopted a stacked generalization approach to anti-spam filtering
W15-3807 classifiers . Our results show that the stacked generalization learner performs better than
P13-2060 Casacuberta , 2008 ) . Stacking ( or stacked generalization ) ( Wolpert , 1992 ) is another
W15-3807 needed in this work . <title> Stacked Generalization for Medical Concept Extraction
P13-2098 ensemble learning method similar to stacked generalization ( Wolpert , 1992 ) and Combiner
D13-1041 training for global predictor g1 Stacked generalization ( Wolpert , 1992 ) is a meta
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