54 search results - page 2 / 11 » A Comparison of the Bagging and the Boosting Methods Using t... |
ICML
1999
IEEE
14 years 5 months ago
1999
IEEE
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
ROCAI
2004
Springer
13 years 10 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,...
IJCAI
2003
13 years 6 months ago
2003
Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the memb...
PRL
2008
13 years 4 months ago
2008
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
CIDM
2009
IEEE
13 years 11 months ago
2009
IEEE
— Identifying faulty classes in object-oriented software is one of the important software quality assurance activities. This paper empirically investigates the application of t...