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» Calibrating Random Forests
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ICONIP
2008
13 years 10 months ago
The Diversity of Regression Ensembles Combining Bagging and Random Subspace Method
Abstract. The concept of Ensemble Learning has been shown to increase predictive power over single base learners. Given the bias-variancecovariance decomposition, diversity is char...
Alexandra Scherbart, Tim W. Nattkemper
CVPR
2008
IEEE
14 years 10 months ago
Extrinsic and depth calibration of ToF-cameras
Recently, ToF-cameras have attracted attention because of their ability to generate a full 21 2 D depth image at video frame rates. Thus, ToF-cameras are suitable for real-time 3D...
Stefan Fuchs, Gerd Hirzinger
PAMI
2000
127views more  PAMI 2000»
13 years 8 months ago
Geometric Camera Calibration Using Circular Control Points
Modern CCD cameras are usually capable of a spatial accuracy greater than 1/50 of the pixel size. However, such accuracy is not easily attained due to various error sources that c...
Janne Heikkilä
ECAI
2008
Springer
13 years 10 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
TRECVID
2008
13 years 10 months ago
Oxford/IIIT TRECVID 2008 - Notebook paper
The Oxford/IIIT team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or aud...
James Philbin, Manuel J. Marín-Jimén...