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» Bagging, Boosting, and C4.5
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ICML
1999
IEEE
14 years 8 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
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...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
BMCBI
2004
176views more  BMCBI 2004»
13 years 7 months ago
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
Kai Huang, Robert F. Murphy
CVPR
2009
IEEE
15 years 2 months ago
Multiple Instance Feature for Robust Part-based Object Detection
Feature misalignment in object detection refers to the phenomenon that features which re up in some positive detection windows do not re up in other pos- itive detection windo...
Zhe Lin (University of Maryland at College Park), ...
JMLR
2002
144views more  JMLR 2002»
13 years 7 months ago
Round Robin Classification
In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifie...
Johannes Fürnkranz
ICPR
2010
IEEE
13 years 11 months ago
Inverse Multiple Instance Learning for Classifier Grids
Abstract--Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one drawback of such approaches is drifting if a...
Sabine Sternig, Peter M. Roth, Horst Bischof