14 search results - page 2 / 3 » Bayesian Averaging of Classifiers and the Overfitting Proble... |
FLAIRS
2008
13 years 7 months ago
2008
Naive Bayesian classifiers work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelev...
ICML
2005
IEEE
14 years 6 months ago
2005
IEEE
In this paper, we compare both discriminative and generative parameter learning on both discriminatively and generatively structured Bayesian network classifiers. We use either ma...
MICCAI
2006
Springer
14 years 6 months ago
2006
Springer
This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. We propose a general mesh-based atlas representation, and compare diff...
ICML
1999
IEEE
14 years 6 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...
ASIAN
2007
Springer
13 years 11 months ago
2007
Springer
Extensive use of computer networks and online electronic data and high demand for security has called for reliable intrusion detection systems. A repertoire of different classifier...