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EUROCOLT
1995
Springer
13 years 11 months ago
A decision-theoretic generalization of on-line learning and an application to boosting
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
Yoav Freund, Robert E. Schapire
GECCO
2005
Springer
139views Optimization» more  GECCO 2005»
14 years 1 months ago
Event-driven learning classifier systems for online soccer games
This paper reports on the application of classifier systems to the acquisition of decision-making algorithms for agents in online soccer games. The objective of this research is t...
Yuji Sato, Ryutaro Kanno
ICALT
2009
IEEE
13 years 11 months ago
Using Students' Devices and a No-to-Low Cost Online Tool to Support Interactive Experiential mLearning
The rapid evolution and ubiquitous use of mobile devices is an historical opportunity to improve experiential interactivity in education practices to support “deep” learning. ...
Andrew Litchfield, Ryszard Raban, Laurel Evelyn Dy...
ALT
2008
Springer
14 years 4 months ago
Learning with Continuous Experts Using Drifting Games
We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the ...
Indraneel Mukherjee, Robert E. Schapire
CVPR
2010
IEEE
14 years 3 months ago
On-line Semi-supervised Multiple-Instance Boosting
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deli...
Bernhard Zeisl, Christian Leistner, Amir Saffari, ...