28 search results - page 1 / 6 » How boosting the margin can also boost classifier complexity |
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...
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
Boosting is a popular approach for building accurate classifiers. Despite the initial popular belief, boosting algorithms do exhibit overfitting and are sensitive to label noise. ...
ICPR
2008
IEEE
14 years 5 months ago
2008
IEEE
On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-li...
AAAI
1998
13 years 6 months ago
1998
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
COLT
2000
Springer
13 years 9 months ago
2000
Springer
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...