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AI
2010
Springer
13 years 2 days ago
Robustness of Classifiers to Changing Environments
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
AI
2008
Springer
13 years 7 months ago
Assessing the Impact of Changing Environments on Classifier Performance
Abstract. The purpose of this paper is to test the hypothesis that simple classifiers are more robust to changing environments than complex ones. We propose a strategy for generati...
Rocío Alaíz-Rodríguez, Nathal...
CVPR
2009
IEEE
1002views Computer Vision» more  CVPR 2009»
15 years 5 days 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...
CORR
2000
Springer
84views Education» more  CORR 2000»
13 years 4 months ago
Robust Classification for Imprecise Environments
In real-world environments it usually is difficult to specify target operating conditions precisely, for example, target misclassification costs. This uncertainty makes building ro...
Foster J. Provost, Tom Fawcett
IEICET
2010
113views more  IEICET 2010»
13 years 3 months ago
Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements
This paper presents PrBPRRC (Probabilistic Bipolar Radial Reach Correlation), a change detection method that is robust against illumination changes and background movements. Most ...
Kentaro Yokoi