45 search results - page 5 / 9 » Boosting in the Limit: Maximizing the Margin of Learned Ense... |
ACL
2006
13 years 9 months ago
2006
We present a learning framework for structured support vector models in which boosting and bagging methods are used to construct ensemble models. We also propose a selection metho...
KAIS
2010
13 years 6 months ago
2010
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of b...
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,...