Modeling Unsupervised Learning with SUSTAIN

Todd M. Gureckis and Bradley C. Love

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. This paper extends SUSTAIN so that it can be used to model unsupervised learning data. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. Two seemingly contradictory unsupervised learning data sets are modeled using this new recruitment method. In addition, the feasibility of using a unified recruitment method for both supervised and unsupervised learning is discussed.

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