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PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
15 years 7 months ago
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
Philippe Rolet, Michèle Sebag, Olivier Teyt...
114
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ICML
2000
IEEE
16 years 3 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. ...
114
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BIOSYSTEMS
2007
72views more  BIOSYSTEMS 2007»
15 years 2 months ago
On the use of multi-objective evolutionary algorithms for survival analysis
This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approxima...
Christian Setzkorn, Azzam Fouad George Taktak, Ber...
211
Voted
ACID
2006
220views Algorithms» more  ACID 2006»
15 years 4 months ago
Vertex and Edge Covers with Clustering Properties: Complexity and Algorithms
We consider the concepts of a t-total vertex cover and a t-total edge cover (t 1), which generalize the notions of a vertex cover and an edge cover, respectively. A t-total verte...
Henning Fernau, David Manlove
140
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NIPS
2001
15 years 4 months ago
Model-Free Least-Squares Policy Iteration
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Michail G. Lagoudakis, Ronald Parr