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  Gaussian process change point models

Saatci, Y., Turner, R., & Rasmussen, C. (2010). Gaussian process change point models. In J. Fürnkranz, & T. Joachims (Eds.), 27th International Conference on Machine Learning (ICML 2010) (pp. 927-934). Madison, WI, USA: Omnipress.

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 Creators:
Saatci, Y, Author
Turner, R, Author
Rasmussen, CE1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.

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 Dates: 2010-06
 Publication Status: Published in print
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Title: 27th International Conference on Machine Learning (ICML 2010)
Place of Event: Haifa, Israel
Start-/End Date: 2010-06-21 - 2010-06-24

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Title: 27th International Conference on Machine Learning (ICML 2010)
Source Genre: Proceedings
 Creator(s):
Fürnkranz, J, Editor
Joachims, T, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : Omnipress
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 927 - 934 Identifier: ISBN: 978-1-60558-907-7