Direct optimization of margins improves generalization in combined classifiers

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 288-294

  Mason, Llew ^a   Bartlett, Peter ^a   Baxter, Jonathan ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMIZATION;

COMBINED CLASSIFIERS; DIRECT OPTIMIZATION; LIGHT CURVES; MARGIN DISTRIBUTIONS; TEST ERRORS; TRAINING ERRORS;

ADAPTIVE BOOSTING;

EID: 84879873079     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. P. L. Bartlett. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2):525-536, 1998.
2. L. Breiman. Prediction games and arcing algorithms. Technical Report 504, Department of Statistics, University of California, Berkeley, 1997.
3. E. Keogh C. Blake and C.J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
4. A. Grove and D. Schuurmans. Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 692-699, 1998.
5. L. Mason, P. L. Bartlett, and J. Baxter. Improved generalization through explicit optimization of margins. Technical report, Department of Systems Engineering, Australian National University, 1998. (Available from http://syseng.anu.edu.au/lsg).
6. R. E. Schapire, Y. Freund, P. L. Bartlett, and W. S. Lee. Boosting the margin: a new explanation for the effectiveness of voting methods. Annals of Statistics, (to appear), 1998.