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ICASSP
2011
IEEE
12 years 11 months ago
SRF: Matrix completion based on smoothed rank function
In this paper, we address the matrix completion problem and propose a novel algorithm based on a smoothed rank function (SRF) approximation. Among available algorithms like FPCA a...
Hooshang Ghasemi, Mohmmadreza Malek-Mohammadi, Mas...
ICCBR
2005
Springer
14 years 27 days ago
CBR for State Value Function Approximation in Reinforcement Learning
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
Thomas Gabel, Martin A. Riedmiller
NN
2000
Springer
145views Neural Networks» more  NN 2000»
13 years 7 months ago
Best approximation by Heaviside perceptron networks
In Lp-spaces with p [1, ) there exists a best approximation mapping to the set of functions computable by Heaviside perceptron networks with n hidden units; however for p (1, ) ...
Paul C. Kainen, Vera Kurková, Andrew Vogt
SIGMOD
2005
ACM
143views Database» more  SIGMOD 2005»
14 years 7 months ago
Holistic Aggregates in a Networked World: Distributed Tracking of Approximate Quantiles
While traditional database systems optimize for performance on one-shot queries, emerging large-scale monitoring applications require continuous tracking of complex aggregates and...
Graham Cormode, Minos N. Garofalakis, S. Muthukris...
AIPS
2004
13 years 8 months ago
Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...
Branislav Kveton, Milos Hauskrecht