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ICANN
2003
Springer
15 years 11 months ago
Meta-learning for Fast Incremental Learning
Model based learning systems usually face to a problem of forgetting as a result of the incremental learning of new instances. Normally, the systems have to re-learn past instances...
Takayuki Oohira, Koichiro Yamauchi, Takashi Omori
ECTEL
2006
Springer
15 years 10 months ago
Personalization Services in Argumentation Tools: a Catalyst for Learning
Argumentation is considered as an essential element for effective learning since it enables learners to develop their points of view and refine their knowledge. Our aim being to fa...
Christina E. Evangelou, Nikos Karousos, Manolis Tz...
NIPS
1998
15 years 7 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
ICML
2005
IEEE
16 years 7 months ago
Learning Gaussian processes from multiple tasks
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equival...
Kai Yu, Volker Tresp, Anton Schwaighofer
ICML
2003
IEEE
16 years 7 months ago
Action Elimination and Stopping Conditions for Reinforcement Learning
We consider incorporating action elimination procedures in reinforcement learning algorithms. We suggest a framework that is based on learning an upper and a lower estimates of th...
Eyal Even-Dar, Shie Mannor, Yishay Mansour