Online-batch strongly convex multi kernel learning

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 787-794

  Orabona, Francesco ^a,c   Jie, Luo ^b   Caputo, Barbara ^c  

^a UNIVERSITY OF MILAN   (Italy)

^b EPFL   (Switzerland)

^c IDIAP RESEARCH INSTITUTE   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATABASE; COMPUTATIONAL COSTS; CONVERGENCE RATES; FASTER CONVERGENCE; KERNEL METHODS; LARGE-SCALE PROBLEM; MULTI-CLASS; MULTI-KERNEL; OBJECT CATEGORIZATION; STATE-OF-THE-ART PERFORMANCE; TRAINING COMPLEXITY; TRAINING EXAMPLE; TRAINING TIME;

ALGORITHMS; COMPUTER VISION;

CONVERGENCE OF NUMERICAL METHODS;

EID: 77955993905     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5540137     Document Type: Conference Paper

References (27)

1. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO, algorithm. In Proc. ICML, 2004. 2, 3
2. A. Bosch, A. Zisserman, and X. Munoz. Representing shape with a spatial pyramid kernel. In Proc. CIVR, 2007. 1, 7
3. G. Cavallanti, N. Cesa-Bianchi, and C. Gentile. Linear algorithms for online multitask classification. In Proc. COLT, 2008. 5
4. G. Cawley. Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. In Proc. IJCNN, 2006. 7
5. C. C. Chang and C. J. Lin. LIBSVM: A Library for Support Vector Machines, 2001. Software available at www.csie.ntu.edu.tw/~cjlin/libsvm. 5, 6
6. C. B. Do, Q. V. Le, and C.-S. Foo. Proximal regularization for online and batch learning. In Proc. ICML, 2009. 4, 5
7. L. Fei-Fei, R. Fergus, and P. Perona. One-shot learning of object categories. IEEE PAMI, 28(4):594-611, 2004. 6
8. P. Gehler and S. Nowozin. Let the kernel figure it out: Principled learning of pre-processing for kernel classifiers. In Proc. CVPR, 2009. 1
9. P. Gehler and S. Nowozin. On feature combination for multiclass object classification. In Proc. ICCV, 2009. 1, 2, 6, 7, 8
10. D. Hush, P. Kelly, C. Scovel, and I. Steinwart. QP algorithms with guaranteed accuracy and run time for support vector machines. JMLR, 7, 2006. 1, 4
11. S. Kakade, S. Shalev-Shwartz, and A. Tewari. On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization. Technical report, TTI, 2009. 3, 5, 8
12. M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, and A. Zien. Efficient and accurate lp-norm multiple kernel learning. In Proc. NIPS. 2009. 1, 3, 8
13. A. Kumar and C. Sminchisescu. Support kernel machines for object recognition. In Proc. ICCV, 2007. 1
14. G. Lanckriet, N. Cristianini, P. Bartlett, and L. E. Ghaoui. Learning the kernel matrix with semidefinite programming. JMLR, 5, 2004. 2
15. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86:2278-2324, 1998. 6, 7
16. M. E. Nilsback and B. Caputo. Cue integration through discriminative accumulation. In Proc. CVPR, 2004. 1, 2
17. M.-E. Nilsback and A. Zisserman. A visual vocabulary for flower classification. In Proc. CVPR, 2006. 6
18. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet. SimpleMKL,. JMLR, 9:2491-2521, November 2008. 1, 2, 3, 6
19. S. Shalev-Shwartz and Y. Singer. Logarithmic regret algorithms for strongly convex repeated games. Technical Report 2007-42, The Hebrew University, 2007. 3, 5
20. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In Proc. ICML, 2007. 3, 4, 5, 6
21. S. Shalev-Shwartz and N. Srebro. SVMoptimization: inverse dependence on training set size. In Proc. ICML, 2008. 2, 4, 6
22. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. JMLR, 7, 2006. 1, 2, 3, 6
23. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proc. ICML, 2004. 1, 2, 3, 8
24. M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. In Proc. ICCV, 2007. 1, 2
25. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In Proc. ICCV, 2009. 1
26. D. Wolpert. Stacked generalization. Neural Networks, 5(2), 1992. 2
27. A. Zien and C. S. Ong. Multiclass multiple kernel learning. In Proc. ICML, 2007. 1, 2, 3