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ATAL
2008
Springer
15 years 6 months ago
Graph Laplacian based transfer learning in reinforcement learning
The aim of transfer learning is to accelerate learning in related domains. In reinforcement learning, many different features such as a value function and a policy can be transfer...
Yi-Ting Tsao, Ke-Ting Xiao, Von-Wun Soo
AH
2004
Springer
15 years 10 months ago
Developing Active Learning Experiences for Adaptive Personalised eLearning
Developing adaptive, rich-media, eLearning courses tends to be a complex, highly-expensive and time-consuming task. A typical adaptive eLearning course will involve a multi-skilled...
Declan Dagger, Vincent P. Wade, Owen Conlan
KDD
1995
ACM
148views Data Mining» more  KDD 1995»
15 years 8 months ago
Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning
Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
Philip K. Chan, Salvatore J. Stolfo
AAAI
2006
15 years 5 months ago
Reinforcement Learning with Human Teachers: Evidence of Feedback and Guidance with Implications for Learning Performance
As robots become a mass consumer product, they will need to learn new skills by interacting with typical human users. Past approaches have adapted reinforcement learning (RL) to a...
Andrea Lockerd Thomaz, Cynthia Breazeal
NAACL
2001
15 years 5 months ago
Learning Optimal Dialogue Management Rules by Using Reinforcement Learning and Inductive Logic Programming
Developing dialogue systems is a complex process. In particular, designing efficient dialogue management strategies is often difficult as there are no precise guidelines to develo...
Renaud Lecoeuche