24 search results - page 3 / 5 » Boosting Algorithms for Maximizing the Soft Margin |
ICML
2006
IEEE
14 years 6 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...
COLT
2005
Springer
13 years 11 months ago
2005
Springer
Abstract. We present several results related to ranking. We give a general margin-based bound for ranking based on the L∞ covering number of the hypothesis space. Our bound sugge...
NIPS
2003
13 years 7 months ago
2003
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an associated simplified nonlinear iterated map and analyze its behavior in lo...
ICML
2004
IEEE
14 years 6 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. ...
ICML
2010
IEEE
13 years 3 months ago
2010
IEEE
We introduce into the classical Perceptron algorithm with margin a mechanism of unlearning which in the course of the regular update allows for a reduction of possible contributio...