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ICML
2007
IEEE
14 years 9 months ago
Multi-task reinforcement learning: a hierarchical Bayesian approach
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
MICCAI
2009
Springer
14 years 10 months ago
Two-Tensor Tractography Using a Constrained Filter
We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber ori...
James G. Malcolm, Martha Elizabeth Shenton, Yoge...
NIPS
2008
13 years 10 months ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater
ICMLA
2008
13 years 10 months ago
Semi-supervised IFA with Prior Knowledge on the Mixing Process: An Application to a Railway Device Diagnosis
Independent Factor Analysis (IFA) is a well known method used to recover independent components from their linear observed mixtures without any knowledge on the mixing process. Su...
Etienne Côme, Zohra Leila Cherfi, Latifa Ouk...
AVSS
2007
IEEE
14 years 3 months ago
Vehicular traffic density estimation via statistical methods with automated state learning
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a R...
Evan Tan, Jing Chen