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We consider feature selection in the semi-supervised learning setting. This problem is rarely addressed in the literature. We propose a new algorithm as a natural extension of the recently developed Logistic I-RELIEF algorithm. The basic idea of the proposed algorithm is to modify the objective function of Logistic I-RELIEF to include the margins of unlabeled samples by following the large margin...
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