An improved training algorithm for the linear ranking support vector machine

Authors:
Antti Airola;Tapio Pahikkala;Tapio Salakoski
Affiliations:
University of Turku and Turku Centre for Computer Science, Turku, Finland;University of Turku and Turku Centre for Computer Science, Turku, Finland;University of Turku and Turku Centre for Computer Science, Turku, Finland
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Abstract

We introduce an O(ms+mlog(m)) time complexity method for training the linear ranking support vector machine, where m is the number of training examples, and s the average number of non-zero features per example. The method generalizes the fastest previously known approach, which achieves the same efficiency only in restricted special cases. The excellent scalability of the proposed method is demonstrated experimentally.