인간의 준지도 학습

Zaki and Nosofsky conducted a behavioral experiment that demonstrates the influence of unlabeled test data on learning.


Advances in machine learning may shed light on the cognitive process behind human learning, which may in turn lead to novel machine learning approaches. Such synergy has long been recognized [130, 105]. The study of human semi-supervised learning is still in its infancy. In the cognitive psychology literature, there have been observations on the effect of unlabeled data to supervised learning, although it was not discussed in a formal semi-supervised learning framework. For examples, see [127, 137, 175, 194]. The earliest quantitative study of human learning with reference to modern semi-supervised machine learning models is [166]. It used drawings of artificial fish to show that human categorization behavior can be influenced by the presence of unlabeled instances. Though certainly suggestive, the experiment had two limitations. First, it used a single
positive labeled example and no negative labeled examples, making it a one-class setting similar to novelty detection or quantile estimation, instead of classification. Second, since the stimuli are representations of a familiar concept (i.e., fish), it is difficult to know whether the results reflect prior knowledge about the category, or new learning obtained over the course of the experiment.The first clear demonstration of human semi-supervised learning is [214]. The combination of active learning and semi-supervised learning has attracted attention in machine learning, see, e.g., [85, 126, 132, 183, 203, 202, 213]. In cognitive science, quantitative research on human active learning has just started [31, 103].We expect the study of the combination to be a fruitful direction.