Voted Spheres: An Online, Fast Approach to Large Scale Learning

Bassam Farran*, Craig Saunders

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we introduce a novel, non-linear, fast, online algorithm for learning on large data sets. This algorithm, which we call Voted Spheres (VS) is a combination of hypersphere-fitting, and the idea of voting. The algorithm builds hyperspheres around points, with different hyperspheres belonging to different classes allowed to overlap. The advantages of the algorithm are that it is simple to implement, very efficient, and generalises well while being able to handle millions of data points. For the KDD intrusion detection data set consisting of 494, 020 data points, the linear version of the algorithm requires under a minute on a standard desktop PC and achieves state of the art performance.

Original languageEnglish
Title of host publication2009 INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS: WAINA, VOLS 1 AND 2
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages744-749
Number of pages6
ISBN (Print)978-1-4244-3999-7
Publication statusPublished - 2009
Event23rd International Conference on Advanced Information Networking and Applications Workshops - Bradford, United Kingdom
Duration: 26 May 200929 May 2009

Conference

Conference23rd International Conference on Advanced Information Networking and Applications Workshops
CountryUnited Kingdom
Period26/05/0929/05/09

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