On the consistency of multiclass classification methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3559 LNAI, Issue , 2005, Pages 143-157

  Tewari, Ambuj ^a   Bartlett, Peter L ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

BAYES CONSISTENCY; CLASSIFICATION METHODS; MULTICLASS CLASSIFICATION METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 26944489606     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_10     Document Type: Conference Paper

References (13)

1. Bartlett, P. L., Jordan, M. I. and McAuliffe, J. D.: Large margin classifiers: Convex Loss, Low Noise and Convergence rates. In Advances in Neural Information Processing Systems 16 (2004)
2. Bredensteiner, E.J. and Bennett, K.P.: Multicategory Classification by Support Vector Machines. Computational Optimization and Applications 12 (1999) 35-46
3. Crammer, K. and Singer, Y.: On the Algorithmic Implementation of Kernel-based Vector Machines. Journal of Machine Learning Research 2 (2001) 265-292
4. Jiang, W.: Process Consistency for AdaBoost. Annals of Statistics 32:1 (2004) 13-29
5. Lee, Y., Li, Y. and Wahba, G.: Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data. Journal of the American Statistical Association 99:465 (2004) 67-81
6. Lugosi, G. and Vayatis, N.: On the Bayes-risk Consistency of Regularized Boosting Methods. Annals of Statistics 32:1 (2004) 30-55
7. Pollard, D.: Convergence of Stochastic Processes. Springer-Verlag, New York (1984)
8. Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1970)
9. Steinwart, I.: Consistency of Support Vector Machines and Other Regularized Kernel Classifiers. IEEE Transactions on Information Theory 51:1 (2005) 128-142
10. Weston, J. and and Watkins, C.: Multi-class support vector machines. Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway College, University of London (1998)
11. Zhang, T.: An Infinity-sample Theory For Multi-category Large Margin Classification. In Advances in Neural Information Processing Systems 16 (2004)
12. Zhang, T.: Statistical Analysis of Some Multi-Category Large Margin Classification Methods. Journal of Machine Learning Research 5 (2004) 1225-1251
13. Zhang, T.: Statistical Behavior and Consistency of Classification Methods Based on Convex Risk Minimization. Annals of Statistics 32:1 (2004) 56-85