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 [BibTeX] [Marc21]
OM-2: An Online Multi-class Multi-kernel Learning Algorithm
Type of publication: Idiap-RR
Citation: Luo_Idiap-RR-06-2010
Number: Idiap-RR-06-2010
Year: 2010
Month: 4
Institution: Idiap
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 does not scale well. In this work we introduce a theo- retically 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, Sim- pleMKL) algorithms.
Projects Idiap
Authors Luo, Jie
Orabona, Francesco
Fornoni, Marco
Caputo, Barbara
Cesa-Bianchi, Nicolo
Crossref by Luo_CVPR-OLCV_2010
Added by: [ADM]
Total mark: 0
  • Luo_Idiap-RR-06-2010.pdf (MD5: ae557a0c4b03a754539e9cf05c0c0109)