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CVPR
2007
IEEE
16 years 5 months ago
Learning Features for Tracking
We treat tracking as a matching problem of detected keypoints between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to i...
Michael Grabner, Helmut Grabner, Horst Bischof
138
Voted
KDD
2009
ACM
178views Data Mining» more  KDD 2009»
16 years 4 months ago
Catching the drift: learning broad matches from clickthrough data
Identifying similar keywords, known as broad matches, is an important task in online advertising that has become a standard feature on all major keyword advertising platforms. Eff...
Sonal Gupta, Mikhail Bilenko, Matthew Richardson
139
Voted
FUZZIEEE
2007
IEEE
15 years 9 months ago
An On-Line Fuzzy Predictor from Real-Time Data
The algorithm of on-line predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-o...
Chih-Ching Hsiao, Shun-Feng Su
ICML
2010
IEEE
15 years 4 months ago
Online Streaming Feature Selection
We study an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available for learni...
Xindong Wu, Kui Yu, Hao Wang, Wei Ding
273
Voted
CVPR
2010
IEEE
15 years 8 months ago
Visual Tracking via Weakly Supervised Learning from Multiple Imperfect Oracles
Long-term persistent tracking in ever-changing environments is a challenging task, which often requires addressing difficult object appearance update problems. To solve them, most...
Bineng Zhong, Hongxun Yao, Sheng Chen, Xiaotong Yu...