45 search results - page 3 / 9 » Boosting in the Limit: Maximizing the Margin of Learned Ense... |
ROCAI
2004
Springer
14 years 1 months ago
2004
Springer
We present an empirical comparison of the AUC performance of seven supervised learning methods: SVMs, neural nets, decision trees, k-nearest neighbor, bagged trees, boosted trees,...
EJASMP
2011
12 years 11 months ago
2011
We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the pho...
NIPS
2003
13 years 9 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 8 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. ...
NIPS
2007
13 years 9 months ago
2007
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...