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» Using model knowledge for learning inverse dynamics
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IJCNN
2007
IEEE
14 years 3 months ago
Dynamic Pooling for the Combination of Forecasts generated using Multi Level Learning
— In this paper we provide experimental results and extensions to our previous theoretical findings concerning the combination of forecasts that have been diversified by three ...
Silvia Riedel, Bogdan Gabrys
ICML
2010
IEEE
13 years 9 months ago
Modeling Interaction via the Principle of Maximum Causal Entropy
The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distribu...
Brian Ziebart, J. Andrew Bagnell, Anind K. Dey
AIPS
2006
13 years 10 months ago
Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
Malcolm J. A. Strens
IROS
2009
IEEE
201views Robotics» more  IROS 2009»
14 years 3 months ago
Modeling tool-body assimilation using second-order Recurrent Neural Network
— Tool-body assimilation is one of the intelligent human abilities. Through trial and experience, humans are capable of using tools as if they are part of their own bodies. This ...
Shun Nishide, Tatsuhiro Nakagawa, Tetsuya Ogata, J...
NN
2008
Springer
169views Neural Networks» more  NN 2008»
13 years 8 months ago
Modeling a flexible representation machinery of human concept learning
dely acknowledged that categorically organized abstract knowledge plays a significant role in high-order human cognition. Yet, there are many unknown issues about the nature of ho...
Toshihiko Matsuka, Yasuaki Sakamoto, Arieta Chouch...