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TNN
2008
82views more  TNN 2008»
15 years 5 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 12 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 6 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
COMGEO
2007
ACM
15 years 5 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 8 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