45 search results - page 5 / 9 » On Feature Selection, Bias-Variance, and Bagging |
ECAI
2008
Springer
13 years 7 months ago
2008
Springer
Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and...
WACV
2008
IEEE
13 years 11 months ago
2008
IEEE
In recent years, many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological in...
ILP
2004
Springer
13 years 10 months ago
2004
Springer
Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well...
ICASSP
2008
IEEE
13 years 11 months ago
2008
IEEE
In this paper, a novel feature for capturing information in a spatio-temporal volume based on regularity flow is presented for action recognition. The regularity flow describes ...
CSDA
2007
13 years 5 months ago
2007
In this paper we address the problem of selecting variables or features in a regression model in the presence of both additive (vertical) and leverage outliers. Since variable sel...