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» Approximate algorithms for neural-Bayesian approaches
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CORR
2007
Springer
128views Education» more  CORR 2007»
15 years 6 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'...
187
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ICASSP
2010
IEEE
15 years 1 months ago
Using reed-muller sequences as deterministic compressed sensing matrices for image reconstruction
An image reconstruction algorithm using compressed sensing (CS) with deterministic matrices of second-order ReedMuller (RM) sequences is introduced. The 1D algorithm of Howard et ...
Kangyu Ni, Somantika Datta, Prasun Mahanti, Svetla...
IPMI
2001
Springer
16 years 7 months ago
Feature Enhancement in Low Quality Images with Application to Echocardiography
In this paper we propose a novel approach to feature enhancement to enhance the quality of noisy images. Our approach is based on a phase-based feature detection algorithm, followe...
Djamal Boukerroui, J. Alison Noble, Michael Brady
ICML
2006
IEEE
16 years 7 months ago
Two-dimensional solution path for support vector regression
Recently, a very appealing approach was proposed to compute the entire solution path for support vector classification (SVC) with very low extra computational cost. This approach ...
Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky
ECML
2004
Springer
15 years 11 months ago
Filtered Reinforcement Learning
Reinforcement learning (RL) algorithms attempt to assign the credit for rewards to the actions that contributed to the reward. Thus far, credit assignment has been done in one of t...
Douglas Aberdeen