Boosting based on a smooth margin

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3120, Issue , 2004, Pages 502-517

  Rudin, Cynthia ^a   Schapire, Robert E ^b   Daubechies, Ingrid ^a  

^a PRINCETON UNIVERSITY   (United States)

^b Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; THEOREM PROVING; VECTORS; ADAPTIVE BOOSTING; APPROXIMATION ALGORITHMS;

APPROXIMATE COORDINATE ASCENT BOOSTING; COORDINATE ASCENT BOOSTING; SUPPORT VECTORS; BOOSTING ALGORITHM; CONVERGENCE RATES; COORDINATE ASCENT; CYCLIC BEHAVIOR; LINE SEARCHES; MAXIMUM MARGIN; SMOOTH APPROXIMATION;

LEARNING SYSTEMS; ALGORITHMS;

EID: 9444251806     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-27819-1_35     Document Type: Conference Paper

References (15)

1. Leo Breiman. Arcing the edge. Technical Report 486, Statistics Department, University of California at Berkeley, 1997.
2. Leo Breiman. Prediction games and arcing algorithms. Neural Computation, 11(7):1493-1517, 1999.
3. Michael Collins, Robert E. Schapire, and Yoram Singer. Logistic regression, AdaBoost and Bregman distances. Machine Learning, 48(1/2/3), 2002.
4. Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997.
5. Adam J. Grove and Dale Schuurmans. Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 1998.
6. V. Koltchinskii and D. Panchenko. Empirical margin distributions and bounding the generalization error of combined classifiers. The Annals of Statistics, 30(1), February 2002.
7. Llew Mason, Peter Bartlett, and Jonathan Baxter. Direct optimization of margins improves generalization in combined classifiers. In Advances in Neural Information Processing Systems 12, 2000.
8. R. Meir and G. Rätsch. An introduction to boosting and leveraging. In S. Mendelson and A. Smola, editors, Advanced Lectures on Machine Learning, pages 119-184. Springer, 2003.
9. Gunnar Rätsch and Manfred Warmuth. Efficient margin maximizing with boosting. Submitted, 2002.
10. Saharon Rosset, Ji Zhu, and Trevor Hastie. Boosting as a regularized path to a maximum margin classifier. Technical report, Department of Statistics, Stanford University, 2003.
11. Cynthia Rudin, Ingrid Daubechies, and Robert E. Schapire. The dynamics of AdaBoost: Cyclic behavior and convergence of margins. Submitted, 2004.
12. Cynthia Rudin, Ingrid Daubechies, and Robert E. Schapire. On the dynamics of boosting. In Advances in Neural Information Processing Systems 16, 2004.
13. Robert E. Schapire. The boosting approach to machine learning: An overview. In MSRI Workshop on Nonlinear Estimation and Classification, 2002.
14. [14] Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5):1651-1686, October 1998.
15. Tong Zhang and Bin Yu. Boosting with early stopping: convergence and consistency. Technical Report 635, Department of Statistics, UC Berkeley, 2003.