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

Luo Jie, Francesco Orabona, Marco Fornoni, Barbara Caputo, Nicolò Cesa-Bianchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations


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 stand ard batch MKL (e.g. SILP, SimpleMKL) algorithms. © 2010 IEEE.
Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Number of pages8
StatePublished - Sep 17 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-25


Dive into the research topics of 'OM-2: An online multi-class Multi-Kernel Learning algorithm'. Together they form a unique fingerprint.

Cite this