ML Anthology

OM-2: An Online Multi-Class Multi-Kernel Learning Algorithm Luo Jie

Francesco Orabona, Marco Fornoni, Barbara Caputo, Nicolò Cesa-Bianchi
CVPRW 2010 pp. 43-50
doi:10.1109/CVPRW.2010.5543766 /cvprw/2010/orabona2010cvprw-om2/

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 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.

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Cite

Text

Orabona et al. "OM-2: An Online Multi-Class Multi-Kernel Learning Algorithm Luo Jie." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543766

Markdown

[Orabona et al. "OM-2: An Online Multi-Class Multi-Kernel Learning Algorithm Luo Jie." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/orabona2010cvprw-om2/) doi:10.1109/CVPRW.2010.5543766

BibTeX

@inproceedings{orabona2010cvprw-om2,
  title     = {{OM-2: An Online Multi-Class Multi-Kernel Learning Algorithm Luo Jie}},
  author    = {Orabona, Francesco and Fornoni, Marco and Caputo, Barbara and Cesa-Bianchi, Nicolò},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2010},
  pages     = {43-50},
  doi       = {10.1109/CVPRW.2010.5543766},
  url       = {https://mlanthology.org/cvprw/2010/orabona2010cvprw-om2/}
}