E97-1056 2 is obtained using Dudani 's weighted voting method . Note that to devise
A00-1024 components are combined using a weighted voting procedure . The system is evaluated
H05-1059 improvement in chunking by conducting weighted voting of multiple SVMs trained with
N01-1025 their dimen - sionality . We apply weighted voting of 8 SVMs - based systems trained
N01-1025 chunk representations . For the weighted voting systems , we introduce a new
N01-1025 achieve higher accuracy , we apply weighted voting of 8 SVM-based systems which
N01-1025 final decision is given by their weighted voting . There are a number of other
D09-1030 results , we experimented with weighted voting . We weighted votes in two ways
D08-1027 the above equation describes a weighted voting rule : each worker 's vote is
D09-1125 in a CS is computed based on a weighted voting score : , = , 1 , ... , = ( )
N01-1025 higher ac - curacy , we also apply weighted voting of 8 SVM - based systems which
C04-1159 makes a final decision by their weighted voting . The features used in our experiments
C00-1051 so far considered only simple weighted voting , which averages the given weight
H89-2049 recognition system through trained , weighted voting schemes . Our sights are aimed
N01-1025 boosting technique is a type of weighted voting scheme , and has been applied
J03-1004 perceptron also uses a sort of weighted voting and could be interpreted as an
J03-1004 be said to implement a kind of weighted voting of factors ) . In Section 4.3
H89-2049 recognizers into one through trained , weighted voting schemes . Several sets of recognizers
E03-1038 classifier by linearly combining , in a weighted voting scheme , many simple and moderately
J12-3005 the basis of majority voting , weighted voting , or more sophisticated decision
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