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» Boosting with Diverse Base Classifiers
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ECAI
2008
Springer
15 years 3 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
KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 2 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
ICIP
2009
IEEE
14 years 11 months ago
Cat face detection with two heterogeneous features
In this paper, we propose a generic and efficient object detection framework based on two heterogeneous features and demonstrate effectiveness of our method for a cat face detecti...
Tatsuo Kozakaya, Satoshi Ito, Susumu Kubota, Osamu...
CVPR
2007
IEEE
16 years 3 months ago
Part-based Face Recognition Using Near Infrared Images
Recently, we developed NIR based face recognition for highly accurate face recognition under illumination variations [10]. In this paper, we present a part-based method for improv...
Ke Pan, ShengCai Liao, Zhijian Zhang, Stan Z. Li, ...
CVPR
2008
IEEE
16 years 3 months ago
Action recognition by learning mid-level motion features
This paper presents a method for human action recognition based on patterns of motion. Previous approaches to action recognition use either local features describing small patches...
Alireza Fathi, Greg Mori