Pareto optimal linear classification

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

Volumn 2006, Issue , 2006, Pages 473-480

  Kim, Seung Jean ^a   Magnani, Alessandro ^a   Samar, Sikandar ^a   Boyd, Stephen ^a   Lim, Johan ^b  

^a Stanford University   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; PROBABILITY; PROBLEM SOLVING; ROBUSTNESS (CONTROL SYSTEMS); UNCERTAIN SYSTEMS;

BI-CRITERION PROBLEMS; CLASSIFICATION CAPABILITIES; KERNEL-BASED CLASSIFICATION; PARETO OPTIMAL LINEAR CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

EID: 33749252200     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Andrew, A., & Mallows, C. (1974). Scale mixtures of normal distributions. Journal of the Royal Statistical Society-Series 5, 47, 99-102.
2. Bach, F., Heckerman, D., & Horvitz, E. (2005). On the path to an ideal ROC curve: Considering cost asymmetry in learning classifiers. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS) (pp. 9-16).
3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
4. Efron, B., & Tibshirani, R. (1993). An introduction to bootstrap. London UK: Chapman and Hall.
5. Genz, A., & Bretz, F. (2001). Methods for the computation of multivariate t-probabilities. Journal of Computational and Graphical Statistics, 11, 950-971.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning, data mining, inference, and prediction. Springer.
7. Huang, K., Yang, H., King, I., Lyu, M., & Chan, L. (2004). The minimum error minimax probability machine. Journal of Machine Learning Research, 5, 1253-1286.
8. Kim, S.-J., Magnani, A., Samar, S., Boyd, S., & Lim, J. (2006). Pareto optimal linear classification. Manuscript. Available from www.stanford.edu/~boyd/pareto_opt_class.html.
9. Lanckriet, G., Ghaoui, L. E., Bhattacharyya, C., & Jordan, M. (2002). A robust minimax approach to classification. Journal of Machine Learning Research, 3, 555-582.
10. Lehmann, E., & Romano, J. (2005). Testing statistical hypotheses. New York: Springer-Verlag.
11. Macskassy, S., Provost, F., & Rosset, S. (2005). ROC confidence bands: An empirical evaluation. Proceedings of the 22nd International Conference on Machine Learning (pp. 537-544). ACM.
12. Marshall, A., & Olkin, I. (1960). Multivariate Chebyshev inequalities. Annals of Mathematical Statistics, 32, 1001-1014.
13. Newman, D., Hettich, S., Blake, C., & Merz, C. (1998). UCI repository of machine learning databases. Available from www.ics.uci.edu/~mlearn/MLRepository.html.
14. Scholkopf, B., & Smola, A. (2002). Learning with kernels. MIT Press, Cambride, MA.
15. Wainwright, M., & Simoncelli, E. (2001). Scale mixtures of Gaussians and the statistics of natural images. In Advances in Neural Information Processing Systems, 12, pp. 855-861, MIT Press.
16. Wu, J., Mullin, M., & Rehg, J. (2005). Linear asymmetric classifier for cascade detectors. Proceedings of the 22nd International Conference on Machine Learning (pp. 988-995). ACM.
17. Zadrozny, B., Langford, J., & Abe, N. (2003). Costsensitive learning by cost-proportionate example weighting. Proceedings of the 3rd International Conference on Data Mining (pp. 435-442). IEEE.
18. Zhu, X., & Wu, X. (2004). Cost-guided class noise handling for effective cost-sensitive learning. Proceedings of the 4th International Conference on Data Mining (pp. 297-304). IEEE.