Bi-level path following for cross validated solution of kernel quantile regression

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 840-847

  Rosset, Saharon ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; CONVEX OPTIMIZATION;

BI-LEVEL OPTIMIZATION; BI-LEVEL PROBLEMS; CONDITIONAL QUANTILES; LOSS FUNCTIONS; PATH FOLLOWING; PREDICTIVE MODELING; QUANTILE LOSS; QUANTILE REGRESSION;

MACHINE LEARNING; ROBOT LEARNING;

BI-LEVEL OPTIMIZATIONS; BI-LEVEL PROBLEMS; CONDITIONAL QUANTILES; LOSS FUNCTIONS; PARAMETERIZED; PATH FOLLOWING; PREDICTIVE MODELING; QUANTILE REGRESSIONS;

EID: 56449126749     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390262     Document Type: Conference Paper

References (18)

1. Buchinsky, M. (1994). Changes in the u.s. wage structure 1963-1987: Application of quantile regression. Econometrica, 62, 405-458.
2. Eide, E., & Showalter, M. (1998). The effect of school quality on student performance: A quantile regression approach. Economics Letters, 58, 345-350.
3. Gunther, L., & Zhu, J. (2005). Efficient computation and model selection for the support vector regression. Neural Computation, 19.
4. Hastie, T., Rosset, S., Tibshirani, R., & Zhu, J. (2004). The complete regularization path of the support vector machine. JMLR, 5, 1391-1415.
5. Hastie, T., Tibshirani, R., & Friedman, J. (2001). Elements of statistical learning. Springer.
6. Kimeldorf, G., & Wahba, G. (1971). Some results on cheby-cheffian spline functions. Journal of Mathematical Analysis and Applications, 33, 82-95.
7. Koenker, R. (2005). Quantile regression. New York : Cambridge University Press.
8. Kunapuli, C., Bennett, K. P., Hu, J., & Pang, J.-S. (2007). Bilevel model selection for support vector machines. CRM Proceedings and Lecture Notes. American Mathematical Society, to appear.
9. Li, Y., Liu, Y., & Zhu, J. (2007). Quantile regression in reproducing kernel hilbert spaces. JASA, 102.
10. Meinshausen, N. (2006). Quantile regression forests. JMLR, 7, 983-999.
11. Perlich, C., Rosset, S., Lawrence, R., & Zadrozny, B. (2007). High quantile modeling for customer wallet estimation with other applications. Proceedings of the Twelfth International Conference on Data Mining, KDD-07.
12. Rosset, S. (2008). Bi-level path following for cross validated solution of kernel quantile regression. In preparation, evolving draft available at www.tau.ac.il/~saharon/papers/cvpath.pdf.
13. Rosset, S., & Zhu, J. (2007). Piecewise linear regularized solution paths. Annals of Statistics, 35.
14. Schölkopf, B., & Smola, A. (2002). Learning with kernels. MIT Press, Cambridge, MA.
15. Sharir, M., & Agarwal, P. K. (1995). Davenport-schinzel sequences and their geometric applications. Cambridge University Press.
16. Smola, A., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199-222.
17. Takeuchi, I., Le, Q., Sears, T., & Smola, A. (2006). Non-parametric quantile estimation. JMLR, 7, 1231-1264.
18. Wang, C., Yeung, D.-Y., & Lochovsky, F. H. (2006). Two-dimensional solution path for support vector regression. Proceedings of the 23rd international conference on Machine learning (pp. 993-1000).