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EASE-AA2 employed a single support vector machine (SVM) model with predictive features selected using a greedy feature selection algorithm.
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We reduced set of features using a greedy backward feature elimination and AIC as a model performance measure.
Finally, we define the dependency between fuzzy decision and condition attributes and employ the dependency to evaluate the significance of a candidate feature, using which a greedy feature subset selection algorithm is designed.
(3) Minimize the global normalized cut using a greedy strategy.
In this work a starting solution for this problem is found by using a greedy algorithm.
One of the attractive features of this coding strategy is that given the ordered channel state sequence, the power allocation for the optimal signaling can be obtained using a greedy procedure.
Wu et al. (2006) searched for the best time offset using a greedy stochastic search mechanism [24].
The space of models is searched using a greedy algorithm.
These local skeletons are then directed using a greedy hill-climbing algorithm.
The solution can also be parallelized (fourth technique) using a greedy algorithm.
The hyperparameters of these multiple sparse priors are optimized using a greedy search.
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