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OM-2: An online multi-class Multi-Kernel Learning algorithm Luo Jie

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4 Author(s)
Orabona, F. ; Idiap Res. Inst., Martigny, Switzerland ; Fornoni, M. ; Caputo, B. ; Cesa-Bianchi, N.

Efficient learning from massive amounts of information is a hot topic in computer vision. Available training sets contain many examples with several visual descriptors, a setting in which current batch approaches are typically slow and does not scale well. In this work we introduce a theoretically motivated and efficient online learning algorithm for the Multi Kernel Learning (MKL) problem. For this algorithm we prove a theoretical bound on the number of multiclass mistakes made on any arbitrary data sequence. Moreover, we empirically show that its performance is on par, or better, than standard batch MKL (e.g. SILP, SimpleMKL) algorithms.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on

Date of Conference:

13-18 June 2010

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