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» On Combining Classifiers
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CVPR
2008
IEEE
15 years 8 days ago
Detection with multi-exit asymmetric boosting
We introduce a generalized representation for a boosted classifier with multiple exit nodes, and propose a method to training which combines the idea of propagating scores across ...
Minh-Tri Pham, V-D. D. Hoang, Tat-Jen Cham
PR
2008
108views more  PR 2008»
13 years 10 months ago
From dynamic classifier selection to dynamic ensemble selection
In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a mul...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
CVPR
2006
IEEE
15 years 8 days ago
Model Order Selection and Cue Combination for Image Segmentation
Model order selection and cue combination are both difficult open problems in the area of clustering. In this work we build upon stability-based approaches to develop a new method...
Andrew Rabinovich, Serge Belongie, Tilman Lange, J...
ECML
2005
Springer
14 years 3 months ago
Error-Sensitive Grading for Model Combination
Abstract. Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of cla...
Surendra K. Singhi, Huan Liu
ICPR
2008
IEEE
14 years 11 months ago
Training sequential on-line boosting classifier for visual tracking
On-line boosting allows to adapt a trained classifier to changing environmental conditions or to use sequentially available training data. Yet, two important problems in the on-li...
Helmut Grabner, Horst Bischof, Jan Sochman, Jiri M...