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ML
2010
ACM
151views Machine Learning» more  ML 2010»
13 years 6 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ICML
2006
IEEE
14 years 8 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
EVOW
2007
Springer
14 years 1 months ago
Modeling Genetic Networks: Comparison of Static and Dynamic Models
Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network mode...
Cristina Rubio-Escudero, Oscar Harari, Oscar Cord&...
JIB
2006
67views more  JIB 2006»
13 years 7 months ago
The implications for Bioinformatics of integration across physical scales
Bioinformatics blossomed with research developments in molecular biology. But as the focus of research moves back up the physical scale to the biology of whole multicellular organ...
T. Charles Hodgman, Y. Ugartechea-Chirino, G. Tans...
BMCBI
2007
128views more  BMCBI 2007»
13 years 7 months ago
A Bayesian nonparametric method for prediction in EST analysis
Background: Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several sta...
Antonio Lijoi, Ramsés H. Mena, Igor Prü...