3 accepted papers
Positive-confidence (Pconf) classification [Ishida et al., 2018] is a promising weakly-supervised learning method which trains a binary classifier only from positive data equipped with confidence. However, in practice, the confidence may be skewed by bias arising in an annotation process. The Pconf…
Unlabeled-unlabeled (UU) classification (du Plessis et al. 2013) allows us to train a binary classifier from two sets of unlabeled data with different class priors. In this paper, we go beyond this scenario and try to train a binary classifier only from a single set of unlabeled data. Our key idea i…
In real-world machine learning applications, we are often faced with a situation where only a small number of training samples is available due to high sampling costs. For instance, prediction of mental states such as drowsiness from physiological information is a typical example. To cope with this…