Kernelizing the output of tree-based methods

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 345-352

  Geurts, Pierre ^a,b   Wehenkel, Louis ^b   D'Alché Buc, Florence ^a  

^a UNIVERSITY OF EVRY   (France)

^b UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; IMAGE RECONSTRUCTION; REGRESSION ANALYSIS;

GRAM MATRIX; KERNELIZATION; OUTPUT KERNEL TREES (OK3); STRUCTURED OUTPUTS;

TREES (MATHEMATICS);

EID: 33749248197     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Blockeel, H., Bruynooghe, M., Dzeroski, S., Ramon, J., & Struyf, J. (2002). Hierarchical multi-classification. KDD-2002 Workshop Notes: MRDM 2002, Workshop on Multi-Relational Data Mining (pp. 21-35).
2. Blockeel, H., De Raedt, L., & Ramon, J. (1998). Top-down induction of clustering trees. Proc. of ICML 1998 (pp. 55-63).
3. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
4. Breiman, L., Friedman, J., Olsen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth International (California).
5. Cortes, C., Mehryar, M., & Weston, J. (2005). A general regression technique for learning transductions. Proceedings of ICML 2005 (pp. 153-160).
6. Dhillon, I., Guan, Y., & Kullis, B. (2004). Kernel k-means, spectral clustering and normalized cuts (Technical Report). University of Austin, Texas.
7. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning Journal (advance access: DOI 10.1007/s10994-006-6226-1).
8. Kondor, R., & Lafferty, J. (2002). Diffusion kernels on graphs and other discrete input. Proc. of ICML 2002 (pp. 315-322).
9. Ralaivola, L., & d'Alché-Buc, F. (2003). Dynamical modeling with kernels for nonlinear time series prediction. Advances in Neural Information Processing Systems.
10. Scholkopf, B., & Smola, A. (2002). Learning with kernels. MIT Press.
11. Segal, M. (1992). Tree structured methods for longitudinal data. Journal of the American Statistical Association, 87, 407-418.
12. Taskar, B., Chatalbashev, V., Koller, D., & Guestrin, C. (2005). Learning structured prediction models: A large margin approach. Proc. of the 22nd International Conference on Machine Learning (pp. 897-904).
13. Todorovski, L., Blockeel, H., & Dzeroski, S. (2002). Ranking with predictive clustering trees. Proc. of the 13th European Conference on Machine Learning (pp. 444-456).
14. Tsochantaridis, I., Joachims, T., Hofmann, T., & Altun, Y. (2005). Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6, 1453-1484.
15. Tsuda, K., Akaho, S., & Asai, K. (2003). The em algorithm for kernel matrix completion with auxiliary data. Journal of machine learning research, 4, 67-81.
16. Vert, J.-P., & Yamanishi, Y. (2004). Supervised graph inference. Advances in Neural Information Processing Systems, 17, 1433-1440.
17. Weston, J., Chapelle, O., Elisseeff, A., Schoelkopf, B., & Vapnik, V. (2002). Kernel dependency estimation. Advances in Neural Information Processing Systems, 15.
18. Weston, J., Schoelkopf, B., & Bousquet, O. (2005). Joint kernel maps. Proceedings of the 8th International Work-Conference on Artificial Neural Networks (pp. 176-191).
19. Yamanishi, Y., Vert, J.-P., & Kanehisa, M. (2004). Protein network inference from multiple genomic data: a supervised approach. Bioinformatics, 20, i363-i370.
20. Yamanishi, Y., Vert, J.-P., & Kanehisa, M. (2005). Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics, 21, i468-i477.