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ICML
2008
IEEE
14 years 11 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...
ICML
2000
IEEE
14 years 11 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. ...
ICALT
2008
IEEE
14 years 4 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...
NIPS
1998
13 years 11 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
AAAI
1994
13 years 11 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