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GECCO
2006
Springer
168views Optimization» more  GECCO 2006»
15 years 8 months ago
A Bayesian approach to learning classifier systems in uncertain environments
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...
CLA
2007
15 years 6 months ago
Policies Generalization in Reinforcement Learning using Galois Partitions Lattices
The generalization of policies in reinforcement learning is a main issue, both from the theoretical model point of view and for their applicability. However, generalizing from a se...
Marc Ricordeau, Michel Liquiere
ACL
2006
15 years 6 months ago
On2L - A Framework for Incremental Ontology Learning in Spoken Dialog Systems
An open-domain spoken dialog system has to deal with the challenge of lacking lexical as well as conceptual knowledge. As the real world is constantly changing, it is not possible...
Berenike Loos
179
Voted
EACL
2006
ACL Anthology
15 years 6 months ago
Using Reinforcement Learning to Build a Better Model of Dialogue State
Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning ...
Joel R. Tetreault, Diane J. Litman
137
Voted
AAAI
1997
15 years 6 months ago
Worst-Case Absolute Loss Bounds for Linear Learning Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. I demonstrateworst-case upper bounds on the absolute loss for the perceptron algorithm and ...
Tom Bylander