Learning from Similarity-Confidence Data

13 Feb 2021 Yuzhou Cao Lei Feng Yitian Xu Bo An Gang Niu Masashi Sugiyama

Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data, where we aim to learn an effective binary classifier from only unlabeled data pairs equipped with confidence that illustrates their degree of similarity (two examples are similar if they belong to the same class)... (read more)

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