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» On Combining Classifiers
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KDD
2001
ACM
192views Data Mining» more  KDD 2001»
14 years 9 months ago
Data mining with sparse grids using simplicial basis functions
Recently we presented a new approach [18] to the classification problem arising in data mining. It is based on the regularization network approach but, in contrast to other method...
Jochen Garcke, Michael Griebel
ICCV
2005
IEEE
14 years 10 months ago
A Supervised Learning Framework for Generic Object Detection in Images
In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly impor...
Saad Ali, Mubarak Shah
KDD
2009
ACM
150views Data Mining» more  KDD 2009»
14 years 9 months ago
Information theoretic regularization for semi-supervised boosting
We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labele...
Lei Zheng, Shaojun Wang, Yan Liu, Chi-Hoon Lee
PAMI
2011
13 years 3 months ago
Cost-Sensitive Boosting
—A novel framework is proposed for the design of cost-sensitive boosting algorithms. The framework is based on the identification of two necessary conditions for optimal cost-sen...
Hamed Masnadi-Shirazi, Nuno Vasconcelos
ICCV
2009
IEEE
15 years 1 months ago
Stabilizing Motion Tracking Using Retrieved Motion Priors
In this paper, we introduce a novel iterative motion tracking framework that combines 3D tracking techniques with motion retrieval for stabilizing markerless human motion captur...
Andreas Baak, Bodo Rosenhahn, Meinard Muller, Hans...