Topics in Cognitive Science 5 (1):132-172 (2013)

Abstract
Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization
Keywords Semi‐supervised learning  Category learning  Machine learning
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DOI 10.1111/tops.12010
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References found in this work BETA

Categories and Concepts.Edward E. Smith & L. Douglas - 1981 - Harvard University Press.
Attention, Similarity, and the Identification–Categorization Relationship.Robert M. Nosofsky - 1986 - Journal of Experimental Psychology: General 115 (1):39-57.
On the Genesis of Abstract Ideas.M. I. Posner & S. W. Keele - 1968 - Journal of Experimental Psychology 77 (2p1):353-363.

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