Top-down decision tree learning as information based boosting

Theoretical Computer Science

Volumn 292, Issue 2, 2003, Pages 447-464

  Takimoto, Eiji ^a   Maruoka, Akira ^a  

^a TOHOKU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION THEORY; INFORMATION THEORY; LEARNING ALGORITHMS; TREES (MATHEMATICS);

BOOSTING TECHNIQUES;

LEARNING SYSTEMS;

EID: 0037467653     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(02)00181-0     Document Type: Conference Paper

References (14)

1. J.A. Aslam, Improving algorithms for boosting, in: Proc. 13th COLT, 2000, pp. 200-207.
2. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth International Group, 1984.
3. Freund Y. Boosting a weak learning algorithm by majority. Inform. and Comput. 121(2):1995;256-285.
4. Y. Freund, R.E. Schapire, Game theory, on-line prediction and boosting, in: Proc. 9th Annu. Conf. on Computer Learning Theory, ACM Press, New York, 1996, pp. 325-332.
5. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Systems Sci. 55(1):1997;119-139.
6. J. Friedman, T. Hastie, R. Tibshirani, Additive logistic regression: a statistical view of boosting, Technical Report, Stanford University, 1998.
7. Kearns M., Mansour Y. On the boosting ability of top-down decision tree learning algorithms. J. Comput. Systems Sci. 58(1):1999;109-128.
8. Y. Mansour, D. McAllester, Boosting using branching programs, in: Proc. 13th COLT, 2000, pp. 220-224.
9. Natarajan B.K. Machine Learning: A Theoretical Approach. 1991;Morgan Kaufmann, San Mateo, CA.
10. Quinlan J.R. C4.5: Programs for Machine Learning. 1993;Morgan Kaufmann, Los Altos, CA.
11. Schapire R.E. The strength of weak learnability. Mach. Learning. 5(2):1990;197-227.
12. R.E. Schapire, Y. Freund, P. Bartlett, W.S. Lee, Boosting the margin: a new explanation for the effectiveness of voting methods, in: Proc. 14th Internat. Conf. on Machine Learning, Morgan Kaufmann, Los Altos, CA, 1997, pp. 322-330.
13. R.E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, in: Proc. 11th Annu. Conf. on Comput. Learning Theory, 1998.
14. E. Takimoto, I. Tajika, A. Maruoka, Mutual information gaining algorithm and its relation to PAC-learning algorithm, in: Proc. 5th Internat. Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence, Vol. 872, Springer, Berlin, 1994, pp. 547-559.