68 search results - page 1 / 14 » On the Margin Explanation of Boosting Algorithms |
COLT
2008
Springer
13 years 6 months ago
2008
Springer
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bou...
ICML
2006
IEEE
14 years 5 months ago
2006
IEEE
Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon i...
IJCAI
2003
13 years 6 months ago
2003
In this paper we propose the framework of Monte Carlo algorithms as a useful one to analyze ensemble learning. In particular, this framework allows one to guess when bagging will ...
ICML
1997
IEEE
14 years 5 months ago
1997
IEEE
TIT
2002
13 years 4 months ago
2002
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...