OM-2: An Online Multi-class Multi-kernel Learning Algorithm
Luo Jie, Francesco Orabona, Marco Fornoni, Barbara Caputo and Nicolò Cesa-Bianchi
In: 4th IEEE Online Learning for Computer Vision Workshop, 13 June 2010, San Francisco, California.
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 eficient 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.