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COLT
2010
Springer
15 years 4 months ago
Regret Minimization With Concept Drift
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...
Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jenni...
ECCV
2000
Springer
16 years 8 months ago
Learning Over Multiple Temporal Scales in Image Databases
Abstract. The ability to learn from user interaction is an important asset for content-based image retrieval (CBIR) systems. Over short times scales, it enables the integration of ...
Nuno Vasconcelos, Andrew Lippman
ICML
2009
IEEE
16 years 7 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
FLAIRS
2007
15 years 9 months ago
Guiding Inference with Policy Search Reinforcement Learning
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily sl...
Matthew E. Taylor, Cynthia Matuszek, Pace Reagan S...
ML
2007
ACM
108views Machine Learning» more  ML 2007»
15 years 6 months ago
Unconditional lower bounds for learning intersections of halfspaces
We prove new lower bounds for learning intersections of halfspaces, one of the most important concept classes in computational learning theory. Our main result is that any statist...
Adam R. Klivans, Alexander A. Sherstov