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ECML
2001
Springer
15 years 7 months ago
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...
Ran El-Yaniv, Oren Souroujon
104
Voted
MVA
2007
15 years 4 months ago
Semi-supervised Incremental Learning of Manipulative Tasks
For a social robot, the ability of learning tasks via human demonstration is very crucial. But most current approaches suffer from either the demanding of the huge amount of label...
Zhe Li, Sven Wachsmuth, Jannik Fritsch, Gerhard Sa...
127
Voted
FLAIRS
2004
15 years 4 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
134
Voted
CVPR
2011
IEEE
14 years 10 months ago
Learning Non-Local Range Markov Random Field for Image Restoration
In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLRMRF). The local spatial range of clique in traditional MRF is extended to th...
Sun Jian, Marshall Tappen
113
Voted
ATAL
2007
Springer
15 years 8 months ago
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
This paper presents the dynamics of multiple reinforcement learning agents from an Evolutionary Game Theoretic (EGT) perspective. We provide a Replicator Dynamics model for tradit...
Liviu Panait, Karl Tuyls