68 search results - page 7 / 14 » On the Margin Explanation of Boosting Algorithms |
ICMLA
2004
13 years 9 months ago
2004
AdaBoost rarely suffers from overfitting problems in low noise data cases. However, recent studies with highly noisy patterns clearly showed that overfitting can occur. A natural s...
ALT
2008
Springer
14 years 4 months ago
2008
Springer
In this paper we discuss boosting algorithms that maximize the soft margin of the produced linear combination of base hypotheses. LPBoost is the most straightforward boosting algor...
ECML
2004
Springer
14 years 1 months ago
2004
Springer
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
ECAI
2006
Springer
13 years 11 months ago
2006
Springer
Scaling discrete AdaBoost to handle real-valued weak hypotheses has often been done under the auspices of convex optimization, but little is generally known from the original boost...
ECCV
2008
Springer
14 years 9 months ago
2008
Springer
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization...