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On the data consumption benefits of accepting increased uncertainty

Martin, Eric, Sharma, Arun, & Stephan, Frank (2004) On the data consumption benefits of accepting increased uncertainty. In Ben-David, Shai, Case, John, & Maruoka, Akira (Eds.) Algorithmic Learning Theory : proceedings of the 15th International Conference, ALT 2004, Springer-Verlag, Padova, Italy, pp. 83-98.

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Abstract

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty, and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exists where the more mind changes the learner is willing to accept, the lesser the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

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ID Code: 37253
Item Type: Conference Paper
Keywords: Mind changes, long term memory, iterative learning, frugal learning
DOI: 10.1007/978-3-540-30215-5_8
ISBN: 9783540233565
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)
Divisions: Current > QUT Faculties and Divisions > Division of Research and Commercialisation
Deposited On: 27 Sep 2010 10:24
Last Modified: 11 Aug 2011 03:37

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