16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04) Data Driven Automatic Model Selection and Parameter Adaptation — A Case Study for Septic Shock Boca Raton, Florida November 15-November 17 ISBN: 0-7695-2236-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2004.47
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated.
Citation:
R. Brause, "Data Driven Automatic Model Selection and Parameter Adaptation — A Case Study for Septic Shock," ictai, pp.278-283, 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), 2004 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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