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 do 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.
OM-2 : an online multi-class multi-kernel learning algorithm / L. Jie, F. Orabona, M. Fornoni, B. Caputo, N. Cesa-Bianchi. ((Intervento presentato al 4. convegno IEEE Online Learning for Computer Vision Workshop tenutosi a San Francisco, USA nel 2010.
OM-2 : an online multi-class multi-kernel learning algorithm
F. OrabonaSecondo
;N. Cesa-BianchiUltimo
2010
Abstract
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 do 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.Pubblicazioni consigliate
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