Towards a unified account of supervised and unsupervised category learning
Abstract
SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the suprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN so that it can be used to account for both supervised and unsupervised learning data through a common mechanism. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. The new formulation of the model is called uSUSTAIN for "unifed SUSTAIN." The implications of using a unified recruitment method for both supervised and unsupervised learning is discussed.
Keywords
Bibtex entry:
@article{gureckis2003towards,
abstract = {SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the suprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category learning can address. This paper extends SUSTAIN so that it can be used to account for both supervised and unsupervised learning data through a common mechanism. A modified recruitment mechanism is introduced that creates new conceptual clusters in response to surprising events during learning. The new formulation of the model is called uSUSTAIN for "unifed SUSTAIN." The implications of using a unified recruitment method for both supervised and unsupervised learning is discussed.},
author = {Gureckis, T.M. and Love, B.C.},
journal = {Journal of Experimental and Theoretical Artificial Intelligence},
number = {1},
pages = {1--24},
publisher = {Taylor and Francis},
title = {Towards a unified account of supervised and unsupervised category learning},
volume = {15},
year = {2003}}QR Code:
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