A geometric approach to leveraging weak learners

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

Volumn 1572, Issue , 1999, Pages 18-33

  Duffy, Nigel ^a   Helmbold, David ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; GRADIENT METHODS;

CONSTRAINED GRADIENT DESCENT; EMPIRICAL PERFORMANCE; GEOMETRIC APPROACHES; NATURAL POTENTIAL; NEGATIVE COMPONENTS; POTENTIAL FUNCTION; WEAK LEARNER;

ADAPTIVE BOOSTING;

EID: 84947765278     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-49097-3_3     Document Type: Conference Paper

References (16)

1. N. Abe, J. Takeuchi, and M. K. Warmuth. Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence. In Proc. 4th Annu. Workshop on Comput. Learning Theory, pages 277-289, San Mateo, CA, 1991. Morgan Kaufmann.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proc. 5th Annu. Workshop on Comput. Learning Theory, pages 144-152. ACM Press, New York, NY, 1992.
3. Leo Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
4. Leo Breiman. Arcing the edge. Technical Report 486, Department of Statistics, University of California, Berkeley, 1997. Available at http://www. stat. berkeley. edu.
5. Leo Breiman. Bias, variance, and arcing classifiers. Technical Report 460, Department of Statistics, University of California, Berkeley, 1997. Available at http://www. stat. berkeley. edu.
6. Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121(2): 256-285, September 1995. Also appeared in COLT90.
7. 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.
8. Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Additive logistic regression: a statistical view of boosting. Unpublished manuscript available at http://www-stat. stanford. edu, 1998.
9. Ron Kohavi, Dan Sommerfield, and James Dougherty. Data mining using MLC++: A machine learning library in C++. In Tools with Artificial Intelligence. IEEE Computer Society Press, 1996. http://www. sgi. com/Technology/mlc.
10. D. G. Luenberger. Linear and Nonlinear Programming. Addison-Wesley, Reading, MA, 1984.
11. Llew Mason, Peter Bartlett, and Jonathan Baxter. Improved generalization through explicit optimization of margins. Technical report, Department of Systems Engineering, Research School of Information Sciences and Engineering, Australian National University, 1998.
12. J. R. Quinlan. Bagging, boosting and c4. 5. In Proceedings of the Thirteenth National Conference of Artificial Intelligence, pages 725-730. AAAI Press and the MIT Press, 1996.
13. Gunnar Ratsch, Takashi Onoda, and Klaus-R. Muller. Soft margins for adaboost. Technical Report NC-TR-1998-021, NeuroCOLT2, 1998.
14. Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. In Proc. 14th International Conference on Machine Learning, pages 322-330. Morgan Kaufmann, 1997.
15. Robert E. Schapire and Yoram Singer. Improved boosting algorithms using confidence-rated predictions. In Proc. 11th Annu. Conf. on Comput. learning Theory, 1998.
16. V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, New York, 1982.