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JC
2011
87views more  JC 2011»
14 years 4 months ago
Lower bounds for the complexity of linear functionals in the randomized setting
Abstract. Hinrichs [3] recently studied multivariate integration defined over reproducing kernel Hilbert spaces in the randomized setting and for the normalized error criterion. I...
Erich Novak, Henryk Wozniakowski
ICML
2007
IEEE
15 years 10 months ago
On learning linear ranking functions for beam search
Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for con...
Yuehua Xu, Alan Fern
ICML
2004
IEEE
15 years 10 months ago
Convergence of synchronous reinforcement learning with linear function approximation
Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merk...
Artur Merke, Ralf Schoknecht
JSCIC
2007
89views more  JSCIC 2007»
14 years 9 months ago
Adjoint Recovery of Superconvergent Linear Functionals from Galerkin Approximations. The One-dimensional Case
In this paper, we extend the adjoint error correction of Pierce and Giles [SIAM Review, 42 (2000), pp. 247-264] for obtaining superconvergent approximations of functionals to Gale...
Bernardo Cockburn, Ryuhei Ichikawa
ICRA
2007
IEEE
155views Robotics» more  ICRA 2007»
15 years 4 months ago
Value Function Approximation on Non-Linear Manifolds for Robot Motor Control
— The least squares approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular an...
Masashi Sugiyama, Hirotaka Hachiya, Christopher To...