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COLT
2008
Springer
14 years 11 months ago
More Efficient Internal-Regret-Minimizing Algorithms
Standard no-internal-regret (NIR) algorithms compute a fixed point of a matrix, and hence typically require O(n3 ) run time per round of learning, where n is the dimensionality of...
Amy R. Greenwald, Zheng Li, Warren Schudy
CVPR
2005
IEEE
15 years 11 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
SPEECH
2008
114views more  SPEECH 2008»
14 years 9 months ago
A Reinforcement Learning approach to evaluating state representations in spoken dialogue systems
Although dialogue systems have been an area of research for decades, finding accurate ways of evaluating different systems is still a very active subfield since many leading metho...
Joel R. Tetreault, Diane J. Litman
NIPS
2001
14 years 11 months ago
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or databa...
Paul A. Viola, Michael J. Jones
ECML
2005
Springer
15 years 3 months ago
Severe Class Imbalance: Why Better Algorithms Aren't the Answer
This paper argues that severe class imbalance is not just an interesting technical challenge that improved learning algorithms will address, it is much more serious. To be useful, ...
Chris Drummond, Robert C. Holte