Smooth boosting using an information-based criterion

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

Volumn 4264 LNAI, Issue , 2006, Pages 304-318

  Hatano, Kohei ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA HANDLING; INFORMATION ANALYSIS; LEARNING SYSTEMS;

INFORMATION-BASED CRITERIA; SMOOTH BOOSTING ALGORITHMS;

SIGNAL FILTERING AND PREDICTION;

EID: 33750714073     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11894841_25     Document Type: Conference Paper

References (27)

1. J. A. Aslam. Improving algorithms for boosting. In Proc. 13th Annu. Conference on Comput. Learning Theory, pages 200-207, 2000.
2. Jose L. Balcazar, Yang Dai, and Osamu Watanabe. Provably fast training algorithms for support vector machines. In Proceedings of IEEE International Conference on Data Mining (ICDM'01), pages 43-50, 2001.
3. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth International Group, 1984.
4. Sanjoy Dasgupta and Philip M. Long. Boosting with diverse base classifers. In Proceedings of the 16th Annual Conference on Learning Theory and 7th Kernel Workshop, pages 273-287, 2003.
5. C. Domingo, R. Gavaldà, and O. Watanabe. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery, 6(2):131-152, 2002.
6. C. Domingo and O. Watanabe. MadaBoost: A modification of AdaBoost. In Proceedings of 13th Annual Conference on Computational Learning Theory, pages 180-189, 2000.
7. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the Sixth ACM International Conference on Knowledge Discovery and Data Mining, pages 71-80, 2000.
8. W. Feller. An introduction to probability theory and its applications. Wiley, 1950.
9. Y. Freund. An improved boosting algorithm and its implications on learning complexity. In Proc. 5th Annual ACM Workshop on Computational Learning Theory, pages 391-398. ACM Press, New York, NY, 1992.
10. Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121(2):256-285, 1995.
11. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
12. Yoav Freund. An adaptive version of the boost by majority algorithm. In COLT '99: Proceedings of the twelfth annual conference on Computational learning theory, pages 102-113, 1999.
13. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. Annals of Statisitics, 2:337-374, 2000.
14. D. Gavinsky. Optimally-smooth adaptive boosting and application to agnostic learning. Journal of Machine Learning Research, 2003.
15. K. Hatano and M. K. Warmuth. Boosting versus covering. In Advances in Neural Information Processing Systems 16, 2003.
16. K. Hatano and O. Watanabe. Learning r-of-k functions by boosting. In Proceedings of the 15th International Conference on Algorithmic Learning Theory, pages 114-126, 2004.
17. Kohei Hatano. Smooth boosting using an information-based criterion. Technical Report DOI-TR-225, Department of Informatics, Kyushu University, 2006.
18. M. Kearns and Y. Mansour. On the boosting ability of top-down decision tree learning algorithms. Journal of Computer and System Sciences, 58(1):109-128, 1999.
19. Yishay Mansour and David A. McAllester. Boosting using branching programs. Journal of Computer and System Sciences, 64(1):103-112, 2002.
20. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, 1998.
21. Robert E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197-227, 1990.
22. Robert E. Schapire and Yoram Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999.
23. Tobias Scheffer and Stefan Wrobel. Finding the most interesting patterns in a database quickly by using sequential sampling. Journal of Machine Learning Research, 3:833-862, 2003.
24. R. J. Serfling. Approximation theorems of mathematical statistics. Wiley, 1980.
25. R. A. Servedio. Smooth boosting and learning with malicious noise. In 14th Annual Conference on Computational Learning Theory, pages 473-489, 2001.
26. Eiji Takimoto, Syuhei Koya, and Akira Maruoka. Boosting based on divide and merge. In Proceedings of the 15th International Conference on Algorithmic Learning Theory, pages 127-141, 2004.
27. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.