TY - GEN
T1 - OM-2: An online multi-class Multi-Kernel Learning algorithm
AU - Jie, Luo
AU - Orabona, Francesco
AU - Fornoni, Marco
AU - Caputo, Barbara
AU - Cesa-Bianchi, Nicolò
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-25
PY - 2010/9/17
Y1 - 2010/9/17
N2 - 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.
AB - 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.
UR - http://ieeexplore.ieee.org/document/5543766/
UR - http://www.scopus.com/inward/record.url?scp=77956513732&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2010.5543766
DO - 10.1109/CVPRW.2010.5543766
M3 - Conference contribution
SN - 9781424470297
SP - 43
EP - 50
BT - 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
ER -