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» Optimal Monte Carlo Algorithms
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IJON
2006
131views more  IJON 2006»
13 years 10 months ago
Optimizing blind source separation with guided genetic algorithms
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
J. M. Górriz, Carlos García Puntonet...
ICIP
2007
IEEE
14 years 12 months ago
Image Denoising with Nonparametric Hidden Markov Trees
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
ICIP
2004
IEEE
14 years 11 months ago
Efficient proposal distributions for MCMC image segmentation
We present methods to obtain computationally efficient proposal distributions for Bayesian reversible jump Markov chain Monte Carlo (RJMCMC) based image segmentation. The slow con...
Timo Kostiainen, Jouko Lampinen
ICML
2009
IEEE
14 years 11 months ago
Archipelago: nonparametric Bayesian semi-supervised learning
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a mode...
Ryan Prescott Adams, Zoubin Ghahramani
BIOTECHNO
2008
IEEE
14 years 4 months ago
Grouping Levels of Exposure with Same Observable Effects before Class Prediction in Toxicogenomics
—Gene expression profiling in toxicogenomics is often used to find molecular signature of toxicants. The range of doses chosen in toxicogenomics studies does not always represe...
Vincent Guillemot, Cathy Philippe, Arthur Tenenhau...