A unifying framework for typical multitask multiple kernel learning problems

IEEE Transactions on Neural Networks and Learning Systems

Volumn 25, Issue 7, 2014, Pages 1287-1297

  Li, Cong ^a   Georgiopoulos, Michael ^a   Anagnostopoulos, Georgios C ^b  

^a University of Central Florida   (United States)

^b Florida Institute of Technology   (United States)

Author keywords

Machine learning; optimization methods; pattern recognition; supervised learning; support vector machines (SVMs)

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION; SUPERVISED LEARNING;

KERNEL-BASED LEARNING; LEARNING PROBLEM; MACHINE LEARNING PROBLEM; MULTIPLE KERNEL LEARNING; MULTITASK LEARNING; OPTIMIZATION METHOD; SELECTION TECHNIQUES; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

EID: 84902279861     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2291772     Document Type: Article

References (26)

1. J. Aflalo, A. Ben-Tal, C. Bhattacharyya, J. S. Nath, and S. Raman, "Variable sparsity kernel learning," J. Mach. Learn. Res., vol. 12, pp. 565-592, Feb. 2011.
2. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 27:1-27:27, Apr. 2011
3. C. Cortes, M. Mohri, and A. Rostamizadeh, "L2 regularization for learning kernels," in Proc. 25th Conf. UAI, 2009, pp. 109-116.
4. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, pp. 273-297, 1995.
5. A. Frank and A. Asuncion. (2010). UCI Machine Learning Repository [Online]. Available: http://archive.ics.uci.edu/ml
6. J. Friedman, T. Hastie, and R. Tibshirani. (2010). A Note on the Group Lasso and a Sparse Group Lasso [Online]. Available: http://arxiv.org/abs/1001. 0736
7. M. Gonen and E. Alpaydin, "Multiple kernel learning algorithms," J. Mach. Learn. Res., vol. 12, pp. 2211-2268, Jul. 2011.
8. L. Jacob, F. Bach, and J.-P. Vert, "Clustered multi-task learning: A convex formulation," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2008.
9. T. Jebara, "Multitask sparsity via maximum entropy discrimination," J. Mach. Learn. Res., vol. 12, pp. 75-110, Jan. 2011.
10. Z. Kang, K. Grauman, and F. Sha, "Learning with whom to share in multi-task feature learning," in Proc. 28th ICML, 2011, pp. 1-8.
11. M. Kloft, U. Brefeld, P. Laskov, and S. Sonnenburg, "Non-sparse multiple kernel learning," in Proc. NIPS Workshop Kernel Learn., Autom. Select. Opt. Kernels, 2008, pp. 1-4.
12. M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Muller, and A. Zien, "Efficient and accurate p-norm multiple kernel learning," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2009.
13. G. R. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, Jan. 2004.
14. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "Simple- MKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Sep. 2008.
15. A. Rakotomamonjy, R. Flamary, G. Gasso, and S. Canu, "pq penalty for sparse linear and sparse multiple kernel multitask learning," IEEE Trans. Neural Netw., vol. 22, no. 8, pp. 1307-1320, Aug. 2011.
16. C. Saunders, A. Gammerman, and V. Vovk, "Ridge regression learning algorithm in dual variables," in Proc. 15th ICML, 1998, pp. 515-521.
17. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, and A. J. Smola, "Estimating the support of a high-dimensional distribution," Neural Comput., vol. 13, no. 7, pp. 1443-1471, 2001. (Pubitemid 33595028)
18. L. Tang, J. Chen, and J. Ye, "On multiple kernel learning with multiple labels," in Proc. IJCAI, 2009, pp. 1255-1260.
19. D. M. Tax and R. P. Duin, "Support vector domain description," Pattern Recognit. Lett., vol. 20, nos. 11-13, pp. 1191-1199, 1999. (Pubitemid 32261897)
20. V. Vapnik, Statistical Learning Theory. New York, NY, USA: Wiley, 1998.
21. G. A. Watson, "Globally convergent methods for semi-infinite programming," BIT Numer. Math., vol. 21, no. 3, pp. 362-373, 1981. (Pubitemid 12509041)
22. C. Widmer, N. C. Toussaint, Y. Altun, and G. Ratsch, "Multitask multiple kernel learning (MT-MKL)," in Proc. NIPS Workshop, New Directions Multiple Kernel Learn., 2010, pp. 1-4.
23. X. Xu, I. Tsang, and D. Xu, "Soft margin multiple kernel learning," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 5, pp. 749-761, May 2013.
24. Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu, "Simple and efficient multiple kernel learning by group lasso," in Proc. ICML, 2010, pp. 1175-1182.
25. Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, "Multi-task learning for classification with dirichlet process priors," J. Mach. Learn. Res., vol. 8, pp. 35-63, Jan. 2007. (Pubitemid 46155123)
26. L. W. Zhong and J. T. Kwok, "Convex multitask learning with flexible task clusters," in Proc. 29th ICML, 2012, pp. 1-8.