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COLT
2010
Springer
15 years 2 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...

Book
640views
17 years 3 months ago
Introduction to Pattern Recognition
"Pattern recognition techniques are concerned with the theory and algorithms of putting abstract objects, e.g., measurements made on physical objects, into categories. Typical...
Sargur Srihari
STOC
2003
ACM
154views Algorithms» more  STOC 2003»
16 years 5 months ago
Boosting in the presence of noise
Boosting algorithms are procedures that "boost" low-accuracy weak learning algorithms to achieve arbitrarily high accuracy. Over the past decade boosting has been widely...
Adam Kalai, Rocco A. Servedio
FLAIRS
1998
15 years 6 months ago
Interactively Training Pixel Classifiers
Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. T...
Justus H. Piater, Edward M. Riseman, Paul E. Utgof...
JMLR
2006
109views more  JMLR 2006»
15 years 4 months ago
Some Discriminant-Based PAC Algorithms
A classical approach in multi-class pattern classification is the following. Estimate probability distributions that generated the observations for each label class, and then labe...
Paul W. Goldberg