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» Results of the KDD'99 Classifier Learning
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AAAI
1996
13 years 9 months ago
Building Classifiers Using Bayesian Networks
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competit...
Nir Friedman, Moisés Goldszmidt
AIPR
2003
IEEE
13 years 11 months ago
Sensor and Classifier Fusion for Outdoor Obstacle Detection: an Application of Data Fusion To Autonomous Off-Road Navigation
This paper describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we pre...
Cristian Dima, Nicolas Vandapel, Martial Hebert
CVPR
2009
IEEE
1002views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Classifier Grids for Robust Adaptive Object Detection
In this paper we present an adaptive but robust object detector for static cameras by introducing classifier grids. Instead of using a sliding window for object detection we pro...
Peter M. Roth, Sabine Sternig, Helmut Grabner, Hor...
ICML
2006
IEEE
14 years 8 months ago
How boosting the margin can also boost classifier complexity
Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon i...
Lev Reyzin, Robert E. Schapire
ICIP
1999
IEEE
14 years 9 months ago
Perceptual Grouping of 3-D Features in Aerial Image Using Decision Tree Classifier
We address a new perceptual grouping algorithmfor aerial images, which employs a decision tree classifier and hierarchical multilevel grouping strategy an a bottom-up fashion. In ...
In Kyu Park, Kyoung Mu Lee, Sang Uk Lee