Data driven automatic model selection and parameter adaptation – a case study for septic shock
- 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.
| Author: | Rüdiger W. BrauseGND |
|---|---|
| URN: | urn:nbn:de:hebis:30-79231 |
| Parent Title (German): | Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004) |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2010/09/08 |
| Year of first Publication: | 2004 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Release Date: | 2010/09/08 |
| Source: | IEEE 16th Int. Conf on Tools with Art. Intell. ICTAI-2004, IEEE Press 2004, pp.278-283, (2004) |
| HeBIS-PPN: | 227979044 |
| Institutes: | Informatik und Mathematik / Informatik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
