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AAAI
2008
13 years 10 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
WSC
2004
13 years 9 months ago
Approximating Component Selection
Simulation composability is a difficult capability to achieve due to the challenges of creating components, selecting combinations of components, and integrating the selected comp...
Michael Roy Fox, David C. Brogan, Paul F. Reynolds...
CGF
2008
128views more  CGF 2008»
13 years 8 months ago
Hierarchical Convex Approximation of 3D Shapes for Fast Region Selection
Given a 3D solid model S represented by a tetrahedral mesh, we describe a novel algorithm to compute a hierarchy of convex polyhedra that tightly enclose S. The hierarchy can be b...
Marco Attene, Michela Mortara, Michela Spagnuolo, ...
ISNN
2004
Springer
14 years 1 months ago
Sparse Bayesian Learning Based on an Efficient Subset Selection
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale pr...
Liefeng Bo, Ling Wang, Licheng Jiao
ICMLA
2009
13 years 6 months ago
Structured Prediction with Relative Margin
In structured prediction problems, outputs are not confined to binary labels; they are often complex objects such as sequences, trees, or alignments. Support Vector Machine (SVM) ...
Pannagadatta K. Shivaswamy, Tony Jebara