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» Learning to Solve QBF
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ICML
2008
IEEE
16 years 4 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
120
Voted
ICML
2000
IEEE
16 years 4 months ago
Combining Reinforcement Learning with a Local Control Algorithm
We explore combining reinforcement learning with a hand-crafted local controller in a manner suggested by the chaotic control algorithm of Vincent, Schmitt and Vincent (1994). A c...
Andrew G. Barto, Jette Randløv, Michael T. ...
119
Voted
ICALT
2008
IEEE
15 years 10 months ago
NUCLEO: Adaptive Computer Supported Collaborative Learning in a Role Game Based Scenario
This paper presents the adaptation model used in NUCLEO, a pilot e-learning environment that is currently being developed at the Complutense University of Madrid. The NUCLEO syste...
Pilar Sancho, Rubén Fuentes-Fernánde...
143
Voted
NIPS
1998
15 years 5 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
123
Voted
AAAI
1994
15 years 5 months ago
Improving Learning Performance Through Rational Resource Allocation
This article shows how rational analysis can be used to minimize learning cost for a general class of statistical learning problems. We discuss the factors that influence learning...
Jonathan Gratch, Steve A. Chien, Gerald DeJong