86 search results - page 13 / 18 » Bagging, Boosting, and C4.5 |
MCS
2007
Springer
14 years 1 months ago
2007
Springer
Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost...
ICONIP
2008
13 years 9 months ago
2008
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...
DIS
2009
Springer
14 years 2 months ago
2009
Springer
Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
ICCV
2009
IEEE
15 years 18 days ago
2009
IEEE
In this paper, we investigate the detection of semantic
human actions in complex scenes. Unlike conventional
action recognition in well-controlled environments,
action detection...
ICDM
2009
IEEE
14 years 2 months ago
2009
IEEE
—One common approach to active learning is to iteratively train a single classifier by choosing data points based on its uncertainty, but it is nontrivial to design uncertainty ...