86 search results - page 5 / 18 » Bagging, Boosting, and C4.5 |
IJCAI
2003
13 years 9 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...
ICASSP
2008
IEEE
14 years 2 months ago
2008
IEEE
Bagging is an ensemble method that uses random resampling of a dataset to construct models. In classification scenarios, the random resampling procedure in bagging induces some c...
NECO
2006
13 years 7 months ago
2006
The application of boosting technique to the regression problems has received relatively little attention in contrast to the research aimed at classification problems. This paper ...
ICDM
2003
IEEE
14 years 27 days ago
2003
IEEE
We experimentally evaluate randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches co...
CIARP
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. Diversity is a key characteristic to obtain advantages of combining predictors. In this paper, we propose a modification of bagging to explicitly trade off diversity and ...