ABC-boost: Adaptive base class boost for multi-class classification

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 625-632

  Li, Ping ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BASE CLASS; DATA SETS; MULTI-CLASS CLASSIFICATION; MULTINOMIAL LOGIT MODEL;

ROBOT LEARNING;

EID: 71149083876     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Allwein, E. L., Schapire, R. E., & Singer, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. J. of Machine Learning Research, 1, 113-141.
2. Bartlett, P., Freund, Y., Lee, W. S., & Schapire, R. E. (1998). Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26, 1651-1686.
3. Cossock, D., & Zhang, T. (2006). Subset ranking using regression. Conf. on Learning Theory, 605-619.
4. Freund, Y. (1995). Boosting a weak learning algorithm by majority. Inf. Comput., 121, 256-285.
5. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55, 119-139.
6. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, 1189-1232.
7. Friedman, J. H., Hastie, T. J., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 28, 337-407.
8. Lee, Y., Lin, Y., & Wahba, G. (2004). Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data. J. of Amer. Stat. Asso., 99, 67-81.
9. Li, P., Burges, C. J., & Wu, Q. (2008). Mcrank: Learning to rank using classification and gradient boosting. Neur. Inf. Proc. Sys. Conf. 897-904.
10. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting algorithms as gradient descent. Neur. Inf. Proc. Sys. Conf. 512-518
11. Schapire, R. (1990). The strength of weak learnability. Machine Learning, 5, 197-227.
12. Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37, 297-336.
13. Schapire, R. E., & Singer, Y. (2000). Boostexter: A boosting-based system for text categorization. Machine Learning, 39, 135-168.
14. Tewari, A., & Bartlett, P. L. (2007). On the consistency of multiclass classification methods. J. of Machine Learning Research, 8, 1007-1025.
15. Zhang, T. (2004). Statistical analysis of some multi-category large margin classification methods. J. of Machine Learning Research, 5, 1225-1251.
16. Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K., Sun, G. (2008). A General Boosting Method and its Application to Learning Ranking Functions for Web Search Neur. Inf. Proc. Sys. Conf. 1697-1704.
17. Zou, H., Zhu, J., & Hastie, T. (2008). New multi-category boosting algorithms based on multicategory fisher-consistent losses. The Annals of Applied Statistics, 2, 1290-1306.