Deakin University
Browse

File(s) under permanent embargo

Fast and efficient single pass bayesian learning

conference contribution
posted on 2013-01-01, 00:00 authored by Nayyar ZaidiNayyar Zaidi, Geoff Webb
The 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

Volume

7818

Pagination

149-160

Location

Gold Coast, Qld.

Start date

2013-04-14

End date

2013-04-17

ISBN-13

9783642374531

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

Pei J, Tseng VS, Motoda H, Xu G

Title of proceedings

PAKDD 2013 : Advances in Knowledge Discovery and Data Mining : Proceedings of the 17th Pacific-Asia Conference, Gold Coast, Qld. Part 1

Event

Knowledge, Discovery and Data Mining. Pacific-Asia Conference (17th : 2013 : Gold Coast, Qld.)

Publisher

Springer

Place of publication

Berlin, Germany

Series

Lecture Notes in Computer Science

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC