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TNN
2008
82views more  TNN 2008»
15 years 1 months ago
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find ...
Cristiano Cervellera, Danilo Macciò, Marco ...
JMLR
2010
125views more  JMLR 2010»
14 years 8 months ago
Variational methods for Reinforcement Learning
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
Thomas Furmston, David Barber
IJCAI
1993
15 years 3 months ago
Using Local Information in a Non-Local Way for Mapping Graph-Like Worlds
This paper describes a technique whereby an autonomous agent such as a mobile robot can explore an unknown environment and make a topologicalmapofit. It is assumedthat the environ...
Gregory Dudek, Paul Freedman, Souad Hadjres
110
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COMGEO
2007
ACM
15 years 1 months ago
Learning smooth shapes by probing
We consider the problem of discovering a smooth unknown surface S bounding an object O in R3 . The discovery process consists of moving a point probing device in the free space ar...
Jean-Daniel Boissonnat, Leonidas J. Guibas, Steve ...
ICASSP
2011
IEEE
14 years 5 months ago
Compressed learning of high-dimensional sparse functions
This paper presents a simple randomised algorithm for recovering high-dimensional sparse functions, i.e. functions f : [0, 1]d → R which depend effectively only on k out of d va...
Karin Schnass, Jan Vybíral