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» A selective sampling approach to active feature selection
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IJCNN
2007
IEEE
15 years 4 months ago
Two-stage Multi-class AdaBoost for Facial Expression Recognition
— Although AdaBoost has achieved great success, it still suffers from following problems: (1) the training process could be unmanageable when the number of features is extremely ...
Hongbo Deng, Jianke Zhu, Michael R. Lyu, Irwin Kin...
115
Voted
ISF
2010
164views more  ISF 2010»
14 years 7 months ago
An SVM-based machine learning method for accurate internet traffic classification
Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven t...
Ruixi Yuan, Zhu Li, Xiaohong Guan, Li Xu
110
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INTENV
2010
14 years 5 months ago
Energy Prediction in Smart Environments
In the past decade, smart home environment research has found application in many areas, such as activity recognition, visualization, and automation. However, less attention has be...
Chao Chen, Barnan Das, Diane J. Cook
CVPR
2009
IEEE
16 years 5 months ago
What's It Going to Cost You?: Predicting Effort vs. Informativeness for Multi-Label Image Annotations
Active learning strategies can be useful when manual labeling effort is scarce, as they select the most informative examples to be annotated first. However, for visual category ...
Sudheendra Vijayanarasimhan (University of Texas a...
ICPR
2006
IEEE
15 years 11 months ago
Face Recognition Robust to Head Pose from One Sample Image
Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly con...
Brian C. Lovell, Shaokang Chen, Ting Shan