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» Termination Analysis with Algorithmic Learning
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ICML
2005
IEEE
16 years 4 months ago
Building Sparse Large Margin Classifiers
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Bernhard Schölkopf, Gökhan H. Bakir, Min...
CVPR
2005
IEEE
16 years 5 months ago
WaldBoost - Learning for Time Constrained Sequential Detection
: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of...
Jan Sochman, Jiri Matas
ICCV
2001
IEEE
16 years 5 months ago
Robust Principal Component Analysis for Computer Vision
Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares...
Fernando De la Torre, Michael J. Black
ASPDAC
2005
ACM
99views Hardware» more  ASPDAC 2005»
15 years 5 months ago
A fast counterexample minimization approach with refutation analysis and incremental SAT
- It is a hotly research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. BFL algorithm is the most effective Counterexample minim...
ShengYu Shen, Ying Qin, Sikun Li
STOC
2000
ACM
174views Algorithms» more  STOC 2000»
15 years 8 months ago
Noise-tolerant learning, the parity problem, and the statistical query model
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...
Avrim Blum, Adam Kalai, Hal Wasserman