Place, U. T. (1993j). Unsupervised and supervised learning in neural networks [Unpublished paper presented at the Annual Conference of the Experimental Analysis of Behaviour Group, University College, London, 30th March 1992, and at the Inter-Univerrsity Centre Conference on 'Connectionism and the Philosophy of Mind,' Park Hotel, Bled, Slovenia, 10th June 1993].
[Abstract]The paper examines the relationship between three distinctions, two drawn from the current literature on learning in connectionist networks and one from the animal learning literature: 1. the distinction drawn by connectionists between 'unsupervised' and 'supervised' learning, 2. the distinction also drawn by connectionists between the Hebbian and 'delta' or error-correction learning rules, and 3. the distinction drawn within traditional learning theory between classical or respondent conditioning on the one hand and instrumental or operant learning on the other. It is argued that, despite differences in the way error-correction is applied in the two cases, the distinction between unsupervised and supervised learning corresponds closely to that between classical and instrumental learning. But, whereas unsupervised learning is usually implemented in artificial networks by a version of the Hebbian rule and supervised learning by the 'delta' rule, recent and not so recent work in animal learning suggests that, given plausible assumptions about the arrangement of the network, a version of the Hebbian rule can account for both types of learning.
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