Margin and radius based multiple kernel learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5781 LNAI, Issue PART 1, 2009, Pages 330-343

  Do, Huyen ^a   Kalousis, Alexandros ^a   Woznica, Adam ^a   Hilario, Melanie ^a  

^a UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

Convex optimization; Learning kernel combination; Support vector machines

Indexed keywords

DATA SETS; EMPIRICAL RESULTS; ERROR BOUND; KERNEL FUNCTION; KERNEL METHODS; LEARNING KERNEL COMBINATION; LINEAR COMBINATIONS; MULTIPLE KERNEL LEARNING; OBJECTIVE FUNCTIONS; THEORETICAL ERRORS;

CONVEX OPTIMIZATION; EDUCATION; ROBOT LEARNING;

SUPPORT VECTOR MACHINES;

EID: 70350676631     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04180-8_39     Document Type: Conference Paper

References (21)

1. Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press, Cambridge (2004)
2. Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
3. Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L.E.: Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research 5, 27-72 (2004)
4. Ong, C.S., Smola, A.J., Williamson, R.C.: Learning the kernel with hyperkernels. Journal of Machine Learning Research 6, 1043-1071 (2005)
5. Sonnenburg, S., Ratsch, G., Schafer, C.: A general and efficient multiple kernel learning algorithm. Journal of Machine Learning Research 7, 1531-1565 (2006)
6. Bach, F., Rakotomamonjy, A., Canu, S., Grandvalet, Y.: SimpleMKL. Journal of Machine Learning Research (2008)
7. Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the smo algorithm. In: ICML 2004: Proceedings of the twenty-first international conference on Machine learning, p. 6. ACM, New York (2004)
8. Lanckriet, G., Bie, T.D., Cristianini, N.: A statistical framework for genomic data fusion. Bioinformatics 20 (2004)
9. Cristianini, N., Shawe-Taylor, J., Elisseeff, A.: On kernel-target alignment. Journal of Machine Learning Research (2002)
10. Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46(1-3), 131-159 (2002)
11. Crammer, K., Keshet, J., Singer, Y.: Kernel design using boosting. In: Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2002)
12. Bousquet, O., Herrmann, D.: On the complexity of learning the kernel matrix. In: Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2003)
13. Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
14. Vapnik, V.: Statistical learning theory. Wiley Interscience, Hoboken (1998)
15. Bonnans, J., Shapiro, A.: Optimization problems with perturbation: A guided tour. SIAM Review 40(2), 202-227 (1998)
16. Kalousis, A., Prados, J., Hilario, M.: Stability of feature selection algorithms: a study on high dimensional spaces. Knowledge and Information Systems 12(1), 95-116 (2007)
17. McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 153-157 (1947)
18. Kalousis, A., Theoharis, T.: Noemon: Design, implementation and performance results for an intelligent assistant for classifier selection. Intelligent Data Analysis Journal 3, 319-337 (1999)
19. Leo Liberti, N.M. (ed.): Global Optimization - From Theory to Implementation. Springer, Heidelberg (2006)
20. Collobert, R., Weston, J., Bottou, L.: Trading convexity for scalability. In: Proceedings of the 23th Conference on Machine Learning (2006)
21. Stephen Boyd, L.V. (ed.): Convex optimization. Cambridge University Press, Cambridge (2004)