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ACML
2009
Springer
15 years 11 months ago
Learning Algorithms for Domain Adaptation
A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of tra...
Manas A. Pathak, Eric Nyberg
ITS
2010
Springer
136views Multimedia» more  ITS 2010»
15 years 10 months ago
Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics
Pedagogical tutorial tactics are policies for a tutor to decide the next action when there are multiple actions available. When the contents were controlled so as to be the same, l...
Min Chi, Kurt VanLehn, Diane J. Litman
CVPR
2008
IEEE
15 years 8 months ago
Improving local learning for object categorization by exploring the effects of ranking
Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning ...
Tien-Lung Chang, Tyng-Luh Liu, Jen-Hui Chuang
ECAI
2008
Springer
15 years 8 months ago
Belief revision with reinforcement learning for interactive object recognition
From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. Ho...
Thomas Leopold, Gabriele Kern-Isberner, Gabriele P...
AAAI
2010
15 years 8 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...