Design of a multiple kernel learning algorithm for LS-SVM by convex programming

Neural Networks

Volumn 24, Issue 5, 2011, Pages 476-483

  Jian, Ling ^a,b   Xia, Zhonghang ^b,c   Liang, Xijun ^b   Gao, Chuanhou ^d  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

^b DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^c WESTERN KENTUCKY UNIVERSITY   (United States)

^d ZHEJIANG UNIVERSITY   (China)

Author keywords

Convex optimization; Least squares support vector machines; Multiple kernel learning; Quadratically constrained quadratic programming; Semidefinite programming

Indexed keywords

COMPUTATIONAL COSTS; CONSTRAINED QUADRATIC PROGRAMMING; CONVEX PROGRAMMING; CROSS VALIDATION; EXPERIMENTAL VALIDATIONS; KERNEL BASED METHODS; LEAST SQUARES SUPPORT VECTOR MACHINES; MODEL SELECTION; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; REGULARIZATION PARAMETERS; SEMI-DEFINITE PROGRAMMING; SINGLE KERNEL; UNIFIED FRAMEWORK;

CONSTRAINED OPTIMIZATION; CONVEX OPTIMIZATION; GEARS; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; PARAMETERIZATION; QUADRATIC PROGRAMMING;

SUPPORT VECTOR MACHINES;

ARTICLE; AUTOMATION; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROCESS DESIGN; PROCESS DEVELOPMENT; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; HUMANS; MATHEMATICAL CONCEPTS; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); SOFTWARE; SOFTWARE DESIGN;

EID: 79953713204     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.03.009     Document Type: Article

References (30)

1. Adankon M.M., Cheriet M. Model selection for LS-SVM: application to handwriting recognition. Pattern Recognition 2009, 42:3264-3270.
2. Andersen E.D., Andersen A.D. The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm. High performancee optimization 2000, 197-232. Kluwer Academic Publishers, Norewll, USA. H. Frenk, C. Roos, T. Terlaky, S. Zhang (Eds.).
3. Bach F.R., Lanckriet G.R.G., Jordan M.I. Multiple kernel learning, conic duality, and the SMO algorithm. Proceedings of the twenty-first international conference on machine learning 2004, 6-13. ACM, Banff, Canada. C.E. Brodley (Ed.).
4. Berg C., Christensen J., Ressel P. Harmonic analysis on semigroups: theory of positive definite and related functions 1984, Springer, New York, USA.
5. Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases. University of California. Irvine.
6. Boyd S., Vandenberghe L. Convex optimization 2004, Cambridge University Press, New York, USA.
7. Cawley G.C., Talbot L.C. Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters. Journal of Machine Learning Research 2007, 8:841-861.
8. Conforti D., Guido R. Kernel based support vector machine via semidefinite programming: application to medical diagnosis. Computers & Operations Research 2010, 37:1389-1394.
9. Duan K., Keerthi S.S., Poo A.N. Evaluation of simple performancee measures for tuning SVM hyperparameters. Neurocomputing 2003, 51:41-59.
10. Fung G., Dundar M., Bi J., Rao B. A fast iterative algorithm for Fisher discriminant using heterogeneous kernels. Proceedings of the twenty-first international conference on machine learning 2004, 40-47. ACM, Banff, Canada. C.E. Brodley (Ed.).
11. Golub G.H., Van Loan C.F. Matrix computations 2009, Johns Hopkins University Press, Baltimore, USA.
12. Jian L., Gao C.H., Li L., Zeng J.S. Application of least squares support vector machines to predict the silicon content in blast furnace hot metal. ISIJ International 2008, 48:1659-1661.
13. Kim S.-J., Magnani A., Boyd S. Optimal kernel selection in kernel Fisher discriminant analysis. Proceedings of the twenty-third international conference on machine learning 2006, 465-472. ACM, Pittsburgh, USA. W.W. Cohen, A. Moore (Eds.).
14. Lanckriet G.R.G., Bie T.De., Cristianini N., Jordan M.I., Noble W.S. A statistical framework for genomic data fusion. Bioinformatics 2004, 20:2626-2635.
15. Lanckriet G.R.G., Cristianini N., Bartlett P., Ghaoui L.E., Jordan M.I. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research 2004, 5:27-72.
16. Mewes H.W., Frishman D., Gruber C., Geier B., Haase D., Kaps A., et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Research 2000, 28:37-40.
17. Ojeda F., Suykens J.A.K., De Moor B. Low rank updated LS-SVM classifiers for fast variable selection. Neural Networks 2008, 21:43-449.
18. Ong C.S., Smola A.J., Williamson R.C. Learning the kernel with hyperkernels. Journal of Machine Learning Research 2005, 6:1043-1071.
19. Rakotomamonjy A., Bach F., Canu S., Grandvalet Y. SimpleMKL. Journal of Machine Learning Research 2008, 9:2491-2521.
20. Schölkopf B., Smola A.J. Learning with kernels 2002, MIT Press, Cambridge, USA.
21. Sonnenburg S., Räosch G., Schäer C., Schökopf B. Large scale multiple kernel learning. Journal of Machine Learning Research 2006, 7:1531-1565.
22. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999, 9:293-300. URL: http://www.esat.kuleuven.be/sista/lssvmlab/.
23. Suykens J.A.K., Vandewalle J., De Moor B. Optimal control by least squares support vector machines. Neural Networks 2001, 14:23-35.
24. Sturm J.F. Using SeDuMi 1.02 a MATLAB toolbox for optimization over symmetric cones. Optimization Methods and Software 1999, 11-12:625-653.
25. Tsang I.W., Kwok J.T. Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks 2006, 17:48-58.
26. Van Gestel T., Suykens J.A.K., Baesens B., Viaene S., Vanthienen J., Dedene G., et al. Benchmarking least squares support vector machine classifiers. Machine Learning 2004, 54:5-32.
27. Vandenberghe L., Boyd S. Semidefinite programming. SIAM Review 1996, 38:49-95.
28. Xiang H., Wei Y.M., Diao H.A. Perturbation analysis of generalized saddle point systems. Linear Algebra and its Applications 2006, 419:8-23.
29. Ye J.P., Ji S.W., Chen J.H. Multi-class discriminant kernel learning via convex programming. Journal of Machine Learning Research 2008, 9:719-758.
30. Yu S., Falck T., Daemen A., Tranchevent L.C., Suykens J.A.K., De Moor B., Moreau Y. L2-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics 2010, 11:1-53.