Parsimonious segmentation of time series by Potts models

Autor:	Winkler, G., Kempe, A., Liebscher, V., Wittich, O., Baier, D., Warnecke, K.D.
Jazyk:	angličtina
Rok vydání:	2005
Předmět:	Theoretical computer science Series (mathematics) business.industry Machine learning computer.software_genre Simple (abstract algebra) Data analysis Segmentation Artificial intelligence Focus (optics) business computer Mathematics Potts model
Zdroj:	Innovations in Classification, Data Science, and Information Systems (Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Cottbus, Germany, March 12-14, 2003), Part II, 295-302 STARTPAGE=295;ENDPAGE=302;TITLE=Innovations in Classification, Data Science, and Information Systems (Proceedings of the 27th Annual Conference of the Gesellschaft für Klassifikation e.V., Cottbus, Germany, March 12-14, 2003), Part II Studies in Classification, Data Analysis, and Knowledge Organization ISBN: 3540232214
Popis:	Typical problems in the analysis of data sets like time-series or images crucially rely on the extraction of primitive features based on segmentation. Variational approaches are a popular and convenient framework in which such problems can be studied. We focus on Potts models as simple nontrivial instances. The discussion proceeds along two data sets from brain mapping and functional genomics.
Databáze:	OpenAIRE
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