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ECML
2004
Springer
14 years 3 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
WWW
2006
ACM
14 years 4 months ago
Using proportional transportation similarity with learned element semantics for XML document clustering
This paper proposes a novel approach to measuring XML document similarity by taking into account the semantics between XML elements. The motivation of the proposed approach is to ...
Xiaojun Wan, Jianwu Yang
CIVR
2007
Springer
14 years 4 months ago
Semantics reinforcement and fusion learning for multimedia streams
Fusion of multimedia streams for enhanced performance is a critical problem for retrieval. However, fusion performance tends to easily overfit the hillclimb set used to learn fus...
Dhiraj Joshi, Milind R. Naphade, Apostol Natsev
CIKM
2010
Springer
13 years 8 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
ATAL
2007
Springer
14 years 4 months ago
SMILE: Sound Multi-agent Incremental LEarning
This article deals with the problem of collaborative learning in a multi-agent system. Here each agent can update incrementally its beliefs B (the concept representation) so that ...
Gauvain Bourgne, Amal El Fallah-Seghrouchni, Henry...