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162
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CVPR
2010
IEEE
15 years 10 months ago
Tracking the Invisible: Learning Where the Object Might be
Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a metho...
Helmut Grabner, Jiri Matas, Philippe Cattin, Luc V...
JMLR
2010
144views more  JMLR 2010»
14 years 9 months ago
Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables
In this paper we address the problem of predicting when the available data is incomplete. We show that changing the generally accepted table-wise view of the sample items into a g...
Sándor Szedmák, Yizhao Ni, Steve R. ...
123
Voted
NIPS
2008
15 years 3 months ago
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
PR
2006
99views more  PR 2006»
15 years 2 months ago
Multisets mixture learning-based ellipse detection
We develop an ellipse detection algorithm based on the multisets mixture learning (MML) that differs from the conventional Hough transform perspective. The algorithm developed has...
Zhiyong Liu, Hong Qiao, Lei Xu
IJCNN
2000
IEEE
15 years 6 months ago
Piecewise Linear Homeomorphisms: The Scalar Case
The class of piecewise linear homeomorphisms (PLH) provides a convenient functional representation for many applications wherein an approximation to data is required that is inver...
Richard E. Groff, Daniel E. Koditschek, Pramod P. ...