Generalized multiclass AdaBoost and its applications to multimedia classification

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2006, Issue , 2006, Pages 113-

  Hao, Wei ^a   Luo, Jiebo ^a  

^a EASTMAN KODAK COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); FUNCTIONS; PROBLEM SOLVING; STATISTICAL METHODS;

ADDITIVE MODEL; BOOSTING ALGORITHM; MULTICLASS CLASSIFIER;

MULTIMEDIA SYSTEMS;

EID: 33845525591     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2006.87     Document Type: Conference Paper

References (14)

1. Y. Freund and R. Schapire, "A decision-theoretic generation of on-line learning and an application to boosting," Journal of Computer and System Science, 55(1): 119-139, 1997.
2. J. Friedman, T. Hastie, and R. Tibshirani, "Additive Logistic Regression: a Statistical View of Boosting," Annals of Statistics, 28(2): 337-407, 2000.
3. R. Schapire and Y. Singer, "Improved Boosting Algorithm Using Confidence-rated Prediction," Machine Learning, 37(3): 297-336, 1999.
4. R. Schapire, "Using Output Codes too Boost Multiclass Learning Problems," Proc. 14th International Conference on Machine Learning, 313-321, 1997.
5. V. Guruswami and A. Sahai, "Multiclass learning, boosting, and error-correcting codes," Proc. 12th Annual Conf. Computational Learning Theory, 145-155, 1999.
6. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz, 1998. UCI Repository of machine learning databases.
7. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Tree, Wadsworth, Belmont, CA, 1984.
8. V. Athitsos and S. Sclaroff, "Boosting Nearest Neighbor Classifiers for Multiclass Recognition," Boston University Computer Science Tech. Report, No. 2004-006.
9. A. Passerini, M. Pontil, and P. Frasconi, "From Margins to Probabilities in Multiclass Learning Problems," Proc. 15th European Conf. On Artificial Intelligence, 2002.
10. Y. Sun, S. Todorovic, and J. Li, "Unifying Multi-Class AdaBoost Algorithms with Binary Base Learners under the Margin Framework," Proc. 22nd international conference on Machine learning, 872 -879, 2005.
11. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers," Journal of Machine Learning Research, 113-141, 2001.
12. J. Luo, M. Boutell, R. T. Gray, and C. Brown, "Image Transform Bootstrapping and Its Applications to Semantic Scene Classification," IEEE Trans. Systems, Man, and Cybernetics - Part B 35(3), 2005.
13. B. Scholkopf, C. Burges, and A. Smola. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, MA, 1999.
14. D.M.J. Tax and R.P.W. Duin. Using two-class classifiers for multiclass classification. Proceedings of Int. Conf. on Pattern Recognition, Quebec City, Canada, August 2002.