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» Evaluating learning algorithms and classifiers
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ICIP
2004
IEEE
16 years 5 months ago
Evaluation of shadow classification techniques for object detection and tracking
In a football stadium environment with multiple overhead floodlights, many protruding shadows can be observed originating from each of the targets. To successfully track individua...
John-Paul Renno, James Orwell, Graeme A. Jones
ICPR
2008
IEEE
16 years 5 months ago
Multiple view based 3D object classification using ensemble learning of local subspaces
Multiple observation improves the performance of 3D object classification. However, since the distribution of feature vectors obtained from multiple view points have strong nonlin...
Jianing Wu, Kazuhiro Fukui
ESWA
2008
223views more  ESWA 2008»
15 years 4 months ago
Credit risk assessment with a multistage neural network ensemble learning approach
In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the ...
Lean Yu, Shouyang Wang, Kin Keung Lai
CVPR
2008
IEEE
16 years 6 months ago
Large margin pursuit for a Conic Section classifier
Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision an...
Santhosh Kodipaka, Arunava Banerjee, Baba C. Vemur...
KDD
1998
ACM
113views Data Mining» more  KDD 1998»
15 years 8 months ago
Targeting Business Users with Decision Table Classifiers
Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexi...
Ron Kohavi, Dan Sommerfield