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ICCV
2009
IEEE
14 years 7 months ago
A robust boosting tracker with minimum error bound in a co-training framework
The varying object appearance and unlabeled data from new frames are always the challenging problem in object tracking. Recently machine learning methods are widely applied to tra...
Rong Liu, Jian Cheng, Hanqing Lu
CDC
2009
IEEE
159views Control Systems» more  CDC 2009»
15 years 2 months ago
A distributed machine learning framework
Abstract— A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed in...
Tansu Alpcan, Christian Bauckhage
CVPR
2007
IEEE
15 years 11 months ago
Online Spatial-temporal Data Fusion for Robust Adaptive Tracking
One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target...
Jixu Chen, Qiang Ji
GIS
2006
ACM
15 years 10 months ago
On-line maintenance of simplified weighted graphs for efficient distance queries
We give two efficient on-line algorithms to simplify weighted graphs by eliminating degree-two vertices. Our algorithms are on-line -- they react to updates on the data, keeping t...
Floris Geerts, Peter Z. Revesz, Jan Van den Bussch...
ICML
2004
IEEE
15 years 10 months ago
Online and batch learning of pseudo-metrics
We describe and analyze an online algorithm for supervised learning of pseudo-metrics. The algorithm receives pairs of instances and predicts their similarity according to a pseud...
Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng