Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm

Abstract

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing κ predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a… (More)

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