Human unsupervised and supervised learning as a quantitative distinction
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 surprising 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 to account for both supervised and unsupervised learning data through a common mechanism. The modified model, uSUSTAIN (unified SUSTAIN), is successfully applied to human learning data drawn from Love (2002) that compares unsupervised and supervised learning performance.
Keywords
Bibtex entry:
@article{gureckis2003human,
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 surprising 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 to account for both supervised and unsupervised learning data through a common mechanism. The modified model, uSUSTAIN (unified SUSTAIN), is successfully applied to human learning data drawn from Love (2002) that compares unsupervised and supervised learning performance.},
author = {Gureckis, T.M. and Love, B.C.},
journal = {International Journal of Pattern Recognition and Artificial Intelligence},
number = {05},
pages = {885--901},
publisher = {World Scientific Publishing Company},
title = {Human unsupervised and supervised learning as a quantitative distinction},
volume = {17},
year = {2003}}QR Code:
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