2009 International Conference on Advanced Information Networking and Applications Workshops Voted Spheres: An Online, Fast Approach to Large Scale Learning Bradford, United Kingdom May 26-May 29 ISBN: 978-0-7695-3639-2
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WAINA.2009.79
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.
Index Terms:
online, one pass, intrusion detection, KDD cup, hyperspheres, voting
Citation:
Bassam Farran, Craig Saunders, "Voted Spheres: An Online, Fast Approach to Large Scale Learning," waina, pp.744-749, 2009 International Conference on Advanced Information Networking and Applications Workshops, 2009 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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