What is Gained from Past Learning

What is Gained from Past Learning AbstractWe consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Causal Inference de Gruyter

What is Gained from Past Learning

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Publisher
De Gruyter
Copyright
© 2018 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2193-3685
eISSN
2193-3685
D.O.I.
10.1515/jci-2018-0005
Publisher site
See Article on Publisher Site

Abstract

AbstractWe consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.

Journal

Journal of Causal Inferencede Gruyter

Published: Mar 26, 2018

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