EasyMKL: A scalable multiple kernel learning algorithm

Neurocomputing

Volumn 169, Issue , 2015, Pages 215-224

  Aiolli, Fabio ^a   Donini, Michele ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Feature learning; Feature selection; Kernel methods; Multiple kernel learning

Indexed keywords

FEATURE EXTRACTION; OPTIMIZATION;

FEATURE LEARNING; KERNEL METHODS; MULTIPLE KERNEL LEARNING; MULTIPLE SOURCE; OPTIMIZATION PROBLEMS; SPACE EFFICIENT; STATE OF THE ART; STATE-OF-THE-ART METHODS;

ALGORITHMS;

ARTICLE; CALCULATION; DATA ANALYSIS; DATA SYNTHESIS; EASYMKL; KERNEL METHOD; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION;

EID: 84938202759     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.11.078     Document Type: Article

References (17)

1. Semi-Supervised Learning 2006, MIT Press, Cambridge, MA. http://www.kyb.tuebingen.mpg.de/ssl-book. O. Chapelle, B. Schölkopf, A. Zien (Eds.).
2. Zhu X., Goldberg A.B. Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning 2009, Morgan & Claypool Publishers.
3. Lanckriet G.R.G., Cristianini N., Bartlett P.L., Ghaoui L.E., Jordan M.I. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. 2004, 5:27-72.
4. Gönen M., Alpaydin E. Multiple kernel learning algorithms. J. Mach. Learn. Res. 2011, 12:2211-2268.
5. C. Cortes, M. Mohri, A. Rostamizadeh, Generalization bounds for learning kernels, in: Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel, 2010, pp. 247-254. http://www.icml2010.org/papers/179.pdf.
6. Z. Hussain, J. Shawe-Taylor, Improved loss bounds for multiple kernel learning, in: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011, 2011, pp. 370-377. http://www.jmlr.org/proceedings/papers/v15/hussain11a/hussain11a.pdf.
7. F. Aiolli, G.D.S. Martino, A. Sperduti, A kernel method for the optimization of the margin distribution, in: ICANN (1), 2008, pp. 305-314.
8. K. Bache, M. lichman, Uci machine learning repository (2013). http://archive.ics.uci.edu/ml.
9. A. Vergara, S. Vembu, T. Ayhan, M.A. Ryan, M.L. Homer, R. Huerta, Chemical gas sensor drift compensation using classifier ensembles, 2012.
10. Guyon I., Gunn S., Ben-Hur A., Dror G. Result analysis of the nips 2003 feature selection challenge. Advances in NIPS 17 2005, 545-552. MIT Press, Cambridge, MA. L.K. Saul, Y. Weiss, L. Bottou (Eds.).
11. Xu X., Tsang I.W., Xu D. Soft margin multiple kernel learning. IEEE Trans. Neural Netw. Learn. Syst. 2013, 24(5):749-761.
12. Rakotomamonjy A., Bach F.R., Canu S., Grandvalet Y. Simplemkl. J. Mach. Learn. Res. 2008, 9:2491-2521.
13. A.P. Danyluk, L. Bottou, M.L. Littman (Eds.), Proceedings of ICML 2009, Montreal, Quebec, Canada, June 14-18, 2009, vol. 382 of ACM International Conference Proceeding Series, ACM, 2009.
14. M. Kloft, U. Brefeld, S. Sonnenburg, A. Zien, Non-sparse regularization and efficient training with multiple kernels, CoRR abs/1003.0079.
15. Z. Xu, R. Jin, H. Yang, I. King, M.R. Lyu, Simple and efficient multiple kernel learning by group lasso, in: ICML, 2010, pp. 1175-1182.
16. A. Jain, S.V.N. Vishwanathan, M. Varma, Spg-gmkl: generalized multiple kernel learning with a million kernels, in: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2012.
17. I. Guyon, A.B. Hur, S. Gunn, G. Dror, Result analysis of the nips 2003 feature selection challenge, in: Advances in Neural Information Processing Systems 17, MIT Press, Vancouver, British Columbia, Canada, 2004, pp. 545-552.