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Fast and efficient single pass bayesian learning
conference contribution
posted on 2013-01-01, 00:00 authored by Nayyar ZaidiNayyar Zaidi, Geoff WebbThe rapid growth in data makes ever more urgent the quest for highly scalable learning algorithms that can maximize the benefit that can be derived from the information implicit in big data. Where data are too big to reside in core, efficient learning requires minimal data access. Single pass learning accesses each data point once only, providing the most efficient data access possible without resorting to sampling. The AnDE family of classifiers are effective single pass learners. We investigate two extensions to A2DE, subsumption resolution and MI-weighting. Neither of these techniques require additional data access. Both reduce A2DE’s learning bias, improving its effectiveness for big data. Furthermore, we demonstrate that the techniques are complementary. The resulting combined technique delivers computationally efficient low-bias learning well suited to learning from big data.
History
Event
Knowledge, Discovery and Data Mining. Pacific-Asia Conference (17th : 2013 : Gold Coast, Qld.)Volume
7818Series
Lecture Notes in Computer SciencePagination
149 - 160Publisher
SpringerLocation
Gold Coast, Qld.Place of publication
Berlin, GermanyPublisher DOI
Start date
2013-04-14End date
2013-04-17ISBN-13
9783642374531Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
Jian Pei, Vincent Tseng, Hiroshi Motoda, Guandong XuTitle of proceedings
PAKDD 2013 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 17th Pacific-Asia Conference, Gold Coast, Qld. Part 1Usage metrics
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