CONF Luo_CVPR-OLCV_2010/IDIAP OM-2: An Online Multi-class Multi-kernel Learning Algorithm Luo, Jie Orabona, Francesco Fornoni, Marco Caputo, Barbara Cesa-Bianchi, Nicolo EXTERNAL https://publications.idiap.ch/attachments/papers/2011/Luo_CVPR-OLCV_2010.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Luo_Idiap-RR-06-2010 Related documents In Proceeding of CVPR 2010, Online Learning for Computer Vision Workshop 2010 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. REPORT Luo_Idiap-RR-06-2010/IDIAP OM-2: An Online Multi-class Multi-kernel Learning Algorithm Luo, Jie Orabona, Francesco Fornoni, Marco Caputo, Barbara Cesa-Bianchi, Nicolo EXTERNAL https://publications.idiap.ch/attachments/reports/2010/Luo_Idiap-RR-06-2010.pdf PUBLIC Idiap-RR-06-2010 2010 Idiap April 2010 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.