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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
14 years 1 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
ICPR
2008
IEEE
14 years 8 months ago
Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process
In this work we present a model that uses a Dirichlet Process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient ...
Haijun Ren, Leon N. Cooper, Liang Wu, Predrag Nesk...
PKDD
2010
Springer
162views Data Mining» more  PKDD 2010»
13 years 6 months ago
Expectation Propagation for Bayesian Multi-task Feature Selection
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
Daniel Hernández-Lobato, José Miguel...
SAC
2008
ACM
13 years 7 months ago
Bayesian inference for a discretely observed stochastic kinetic model
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
Richard J. Boys, Darren J. Wilkinson, Thomas B. L....
PRL
2008
118views more  PRL 2008»
13 years 7 months ago
Bayes Machines for binary classification
In this work we propose an approach to binary classification based on an extension of Bayes Point Machines. Particularly, we take into account the whole set of hypotheses that are...
Daniel Hernández-Lobato, José Miguel...