506 search results - page 51 / 102 » Learning Bayesian Networks from Incomplete Databases |
NIPS
2004
14 years 11 months ago
2004
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
RECOMB
2007
Springer
15 years 10 months ago
2007
Springer
Abstract. An important problem in biology is to understand correspondences between mRNA microarray levels and mass spectrometry peptide counts. Recently, a compendium of mRNA expre...
KES
2007
Springer
15 years 4 months ago
2007
Springer
Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, dep...
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AUSAI
2008
Springer
14 years 12 months ago
2008
Springer
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
JMLR
2008
14 years 10 months ago
2008
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...