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AAAI
2011
14 years 6 months ago
Value Function Approximation in Reinforcement Learning Using the Fourier Basis
We describe the Fourier Basis, a linear value function approximation scheme based on the Fourier Series. We empirically evaluate its properties, and demonstrate that it performs w...
George Konidaris, Sarah Osentoski, Philip Thomas
HYBRID
2004
Springer
15 years 11 months ago
Approximations of the Rate of Growth of Switched Linear Systems
The joint spectral radius of a set of matrices is a measure of the maximal asymptotic growth rate that can be obtained by forming long products of matrices taken from the set. This...
Vincent D. Blondel, Yurii Nesterov, Jacques Theys
ICASSP
2009
IEEE
16 years 1 months ago
A low complexity Orthogonal Matching Pursuit for sparse signal approximation with shift-invariant dictionaries
We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionari...
Boris Mailhé, Rémi Gribonval, Fr&eac...
JACIII
2007
99views more  JACIII 2007»
15 years 6 months ago
Asymptotic Behavior of Linear Approximations of Pseudo-Boolean Functions
We study the problem of approximating pseudoBoolean functions by linear pseudo-Boolean functions. Pseudo-Boolean functions generalize ordinary Boolean functions by allowing the fu...
Guoli Ding, Robert F. Lax, Peter P. Chen, Jianhua ...
ICPR
2008
IEEE
16 years 7 months ago
Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process
In this work we present a model that uses a Dirichlet Process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient ...
Haijun Ren, Leon N. Cooper, Liang Wu, Predrag Nesk...