Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
IEEE Transactions on Information Theory - Part 2
Zero-data learning of new tasks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A multilevel approach for learning from labeled and unlabeled data on graphs
Pattern Recognition
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We explore the connections between machine learning and human learning in one form of semi-supervised classification. 22 human subjects completed a novel 2-class categorization task in which they were first taught to categorize a single labeled example from each category, and subsequently were asked to categorize, without feedback, a large set of additional items. Stimuli were visually complex and unrecognizable shapes. The unlabeled examples were sampled from a bimodal distribution with modes appearing either to the left (left-shift condition) or right (right-shift condition) of the two labeled examples. Results showed that, although initial decision boundaries were near the middle of the two labeled examples, after exposure to the unlabeled examples, they shifted in different directions in the two groups. In this respect, the human behavior conformed well to the predictions of a Gaussian mixture model for semi-supervised learning. The human behavior differed from model predictions in other interesting respects, suggesting some fruitful avenues for future inquiry.