86 search results - page 17 / 18 » Bagging, Boosting, and C4.5 |
ECCV
2006
Springer
13 years 11 months ago
2006
Springer
While learning ensembles have been widely used for various pattern recognition tasks, surprisingly, they have found limited application in problems related to medical image analysi...
ESANN
2006
13 years 9 months ago
2006
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
ECCV
2010
Springer
13 years 7 months ago
2010
Springer
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
CLASSIFICATION
2006
13 years 7 months ago
2006
Abstracts "Mixtures at the Interface" David Scott, Rice University Mixture modeling provides an effective framework for complex, high-dimensional data. The potential of m...
IJSI
2008
13 years 7 months ago
2008
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...