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  Machine Learning Methods For Estimating Operator Equations

Steinke, F., & Schölkopf, B. (2006). Machine Learning Methods For Estimating Operator Equations. In IFAC Proceedings Volumes (pp. 1192-1197). Oxford, United Kingdom: Elsevier.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D27F-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-8A55-2
Genre: Conference Paper

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 Creators:
Steinke, F1, 2, Author              
Schölkopf, B1, 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 consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.

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 Dates: 2006-03
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 3640
DOI: 10.3182/20060329-3-AU-2901.00192
 Degree: -

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Title: 14th IFAC Symposium on System Identification (SYSID 2006)
Place of Event: Newcastle, Australia
Start-/End Date: 2006-03-29 - 2006-03-31

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Title: IFAC Proceedings Volumes
Source Genre: Proceedings
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Publ. Info: Oxford, United Kingdom : Elsevier
Pages: - Volume / Issue: 39 (1) Sequence Number: - Start / End Page: 1192 - 1197 Identifier: -