On the data consumption benefits of accepting increased uncertainty
Martin, Eric, Sharma, Arun, & Stephan, Frank (2007) On the data consumption benefits of accepting increased uncertainty. Theoretical Computer Science, 382(3), pp. 170-182.
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 exist where the more mind changes the learner is willing to accept, the less 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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|Item Type:||Journal Article|
|Keywords:||Inductive inference; Mind change bounds; Iterative learning; Memory limitations|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200)
|Divisions:||Current > QUT Faculties and Divisions > Division of Research and Commercialisation|
|Deposited On:||25 Aug 2011 08:12|
|Last Modified:||01 Mar 2012 00:10|
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