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ICML
1997
IEEE
14 years 10 months ago
Hierarchical Explanation-Based Reinforcement Learning
Explanation-Based Reinforcement Learning (EBRL) was introduced by Dietterich and Flann as a way of combining the ability of Reinforcement Learning (RL) to learn optimal plans with...
Prasad Tadepalli, Thomas G. Dietterich
IJCNN
2006
IEEE
14 years 3 months ago
Alleviating Catastrophic Forgetting via Multi-Objective Learning
— Handling catastrophic forgetting is an interesting and challenging topic in modeling the memory mechanisms of the human brain using machine learning models. From a more general...
Yaochu Jin, Bernhard Sendhoff
EWRL
2008
13 years 11 months ago
Policy Learning - A Unified Perspective with Applications in Robotics
Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper,...
Jan Peters, Jens Kober, Duy Nguyen-Tuong
SIGIR
2003
ACM
14 years 2 months ago
Question classification using support vector machines
Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. W...
Dell Zhang, Wee Sun Lee
ICML
2009
IEEE
14 years 10 months ago
Large-scale deep unsupervised learning using graphics processors
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
Rajat Raina, Anand Madhavan, Andrew Y. Ng