A generalized kernel approach to structured output learning

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 471-479

  Kadri, Hachem ^a   Ghavamzadeh, Mohammad ^b   Preux, Philippe ^c  

^a AIX MARSEILLE UNIVERSITY   (France)

^b INRIA   (France)

^c UNIV LILLE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

REGRESSION ANALYSIS;

COVARIANCE OPERATORS; FEATURE SPACE; GENERALIZED KERNELS; IMAGE SPACE; INPUT SPACE; INPUT VARIABLES; REGRESSION METHOD;

LEARNING SYSTEMS;

EID: 84897476865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Álvarez, M., Rosasco, L., and Lawrence, N. D. Kernels for vector-valued functions: a review. Foundations and Trends in Machine Learning, 4(3):195-266, 2012.
2. Bach, F. and Jordan, M. Kernel independent component analysis. Journal of Machine Learning Research, 3:1-48, 2002.
3. Bakir, G., Hofmann, T., Schölkopf, B., Smola, A., Taskar, B., and Vishwanathan, S. (eds.). Predicting Structured Data. MIT Press, 2007.
4. Brouard, C., d'Alché Buc, F., and Szafranski, M. Semi-supervised penalized output kernel regression for link prediction. In Proc. ICML, pp. 593-600, 2011.
5. Caponnetto, A. and De Vito, E. Optimal rates for the regularized least-squares algorithm. Foundations of Computational Mathematics, 7(3):331-368, 2007.
6. Caponnetto, A., Micchelli, C., Pontil, M., and Ying, Y. Universal multi-task kernels. Journal of Machine Learning Research, 68:1615-1646, 2008.
7. Caruana, R. Multitask learning. Machine Learning, 28(1):41-75, 1997. (Pubitemid 127507169)
8. Cortes, C., Mohri, M., and Weston, J. A general regression technique for learning transductions. In Proc. ICML, pp. 153-160, 2005.
9. Cortes, C., Mohri, M., and Weston, J. A General Regression Framework for Learning String-to-String Mappings. MIT Press, 2007.
10. Evgeniou, T., Micchelli, C., and Pontil, M. Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 6:615-637, 2005.
11. Fukumizu, K., Bach, F., and Jordan, M. Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces. Journal of Machine Learning Research, 5:73-99, 2004.
12. Gretton, A., Herbrich, R., Smola, A., Bousquet, O., and Schölkopf, B. Kernel methods for measuring independence. Journal of Machine Learning Research, 6:1-47, 2005.
13. Grunewalder, S., Lever, G., Gretton, A., Baldassarre, L., Patterson, S., and Pontil, M. Conditional mean embeddings as regressors. In Proc. ICML, 2012.
14. Kadri, H., Duflos, E., Preux, P., Canu, S., and Davy, M. Nonlinear functional regression: a functional RKHS approach. In Proc. AISTATS, pp. 111-125, 2010.
15. Kadri, H., Rabaoui, A., Preux, P., Duflos, E., and Rakotomamonjy, A. Functional regularized least squares classification with operator-valued kernels. In Proc. ICML, pp. 993-1000, 2011.
16. Kadri, H., Ghavamzadeh, M., and Preux, P. A generalized kernel approach to structured output learning. Technical Report 00695631, INRIA, 2012.
17. Micchelli, C. and Pontil, M. On learning vector-valued functions. Neural Computation, 17:177-204, 2005. (Pubitemid 40305887)
18. Ramsay, J. and Silverman, B. Functional Data Analysis, 2nd edition. Springer Verlag, New York, 2005.
19. Taskar, B., Guestrin, C., and Koller, D. Max-margin markov networks. In Advances in Neural Information Processing Systems 16. 2004.
20. Troje, N. and Bulthoff, H. Face recognition under varying poses: The role of texture and shape. Vision Research, 36:1761-1771, 1996. (Pubitemid 26166003)
21. Tsochantaridis, I., Joachims, T., Hofmann, T., and Altun, Y. Large margin methods for structured and interdependent output variables. Journal of machine Learning Research, 6:1453-1484, 2005.
22. Wang, Z. and Shawe-Taylor, J. A kernel regression framework for SMT. Machine Translation, 24(2):87-102, 2010.
23. Weston, J., Chapelle, O., Elisseeff, A., Scholkopf, B., and Vapnik, V. Kernel dependency estimation. In Advances in Neural Information Processing Systems 15, pp. 873-880, 2003.
24. Weston, J., BakIr, G., Bousquet, O., Schölkopf, B., Mann, T., and Noble, W. Joint Kernel Maps. MIT Press, 2007.