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Constraining solution space to improve generalization

Published online by Cambridge University Press:  01 March 1997

John A. Bullinaria
Affiliation:
Centre for Speech and Language, Department of Psychology, Birkbeck College, London WC1E 7HX, United [email protected]

Abstract

I suggest that the difficulties inherent in discovering the hidden regularities in realistic (type-2) problems can often be resolved by learning algorithms employing simple constraints (such as symmetry and the importance of local information) that are natural from an evolutionary point of view. Neither “heavy-duty nativism” nor “representational recoding” appear to offer totally appropriate descriptions of such natural learning processes.

Type
Open Peer Commentary
Copyright
© 1997 Cambridge University Press

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