On the design of loss functions for classification: Theory, robustness to outliers, and SavageBoost

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1049-1056

  Masnadi Shirazi, Hamed ^a   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; LEARNING SYSTEMS; STATISTICS;

BOOSTING ALGORITHM; CLASSIFICATION THEORY; CLASSIFIER DESIGN; COMPUTATIONAL TRACTABILITY; CONVENTIONAL METHODS; FUNCTIONAL FORMS; LOGITBOOST; LOSS FUNCTIONS; MACHINE LEARNING PROBLEM; MINIMISATION;

SPECIFICATIONS;

EID: 77956005954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. T. G. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization," Machine Learning, 2000.
2. Y. Wu and Y. Liu, "Robust truncated-hinge-loss support vector machines," JASA, 2007.
3. J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting," Annals of Statistics, 2000.
4. T. Zhang, "Statistical behavior and consistency of classification methods based on convex risk minimization," Annals of Statistics, 2004.
5. P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, classification, and risk bounds," JASA, 2006.
6. L. J. Savage, "The elicitation of personal probabilities and expectations," JASA, vol. 66, pp. 783-801, 1971.
7. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge: Cambridge University Press, 2004.
8. R. McDonald, D. Hand, and I. Eckley, "An empirical comparison of three boosting algorithms on real data sets with artificial class noise," in International Workshop on Multiple Classifier Systems, 2003.
9. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, 1997.
10. T. Zhang and B. Yu, "Boosting with early stopping: Convergence and consistency," Annals of Statistics, 2005.