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ICPR
2000
IEEE
15 years 7 months ago
Feature Learning for Recognition with Bayesian Networks
Many realistic visual recognition tasks are “open” in the sense that the number and nature of the categories to be learned are not initially known, and there is no closed set ...
Justus H. Piater, Roderic A. Grupen
MHVR
1994
119views Multimedia» more  MHVR 1994»
15 years 7 months ago
The Development of a Virtual world for Learning Newtonian Mechanics
We are collaboratively designing "ScienceSpace," a collection of virtual worlds designed to explore the potential utility of physical immersion and multisensory perceptio...
Christopher J. Dede, Marilyn C. Salzman, R. Bowen ...
ECML
2007
Springer
15 years 7 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
FLAIRS
1998
15 years 4 months ago
Optimizing Production Manufacturing Using Reinforcement Learning
Manyindustrial processes involve makingparts with an assemblyof machines, where each machinecarries out an operation on a part, and the finished product requires a wholeseries of ...
Sridhar Mahadevan, Georgios Theocharous
AAAI
1996
15 years 4 months ago
Learning to Take Actions
We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAC-learning results on Occam algorithms hold in this model as well. W...
Roni Khardon