On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data

ICLR 2019 • Nan Lu • Gang Niu • Aditya Krishna Menon • Masashi Sugiyama

Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM... (read more)

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