Semi‐supervised multiple empirical kernel learning with pseudo empirical loss and similarity regularization
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DOI: 10.1002/int.22690
Abstract: Multiple empirical kernel learning (MEKL) is a scalable and efficient supervised algorithm based on labeled samples. However, there is still a huge amount of unlabeled samples in the real‐world application, which are not applicable for… read more here.
Keywords: labeled samples; empirical kernel; unlabeled samples; kernel learning ... See more keywords