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TNN
2008
82views more  TNN 2008»
14 years 10 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 ...
ICASSP
2011
IEEE
14 years 2 months ago
Online speech source separation based on maximum likelihood of local Gaussian modeling
We propose an online speech source separation method which can separate sources under underdetemined conditions. The proposed method is based on local Gaussian modeling (LGM). At ...
Masahito Togami
ICDAR
1999
IEEE
15 years 2 months ago
Heeding More Than the Top Template
We present a method of classifying a pattern using information furnished by a ranked list of templates, rather than just the best matching template. We propose a parsimonious mode...
Prateek Sarkar, George Nagy
CORR
2007
Springer
128views Education» more  CORR 2007»
14 years 10 months ago
Model Selection Through Sparse Maximum Likelihood Estimation
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
EMMCVPR
2001
Springer
15 years 2 months ago
Maximum Likelihood Estimation of the Template of a Rigid Moving Object
Abstract. Motion segmentation methods often fail to detect the motions of low textured regions. We develop an algorithm for segmentation of low textured moving objects. While usual...
Pedro M. Q. Aguiar, José M. F. Moura