313 search results - page 4 / 63 » Boosting with Diverse Base Classifiers |
DIS
2010
Springer
13 years 5 months ago
2010
Springer
Abstract. In multi-instance learning, each example is described by a bag of instances instead of a single feature vector. In this paper, we revisit the idea of performing multi-ins...
ADMA
2006
Springer
13 years 9 months ago
2006
Springer
This paper presents a study on the combination of different classifiers for toxicity prediction. Two combination operators for the Multiple-Classifier System definition are also pr...
PAKDD
2000
ACM
13 years 10 months ago
2000
ACM
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
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 ...
SSPR
2000
Springer
13 years 10 months ago
2000
Springer
To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are...