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  Machine Learning Methods For Estimating Operator Equations

Steinke, F., & Schölkopf, B. (2006). Machine Learning Methods For Estimating Operator Equations. Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), 1-6.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D27F-B Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D280-5
Genre: Konferenzbeitrag

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 Urheber:
Steinke, F1, Autor           
Schölkopf, B1, Autor           
Ninness H. Hjalmarsson, B., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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Schlagwörter: -
 Zusammenfassung: 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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 Datum: 2006-03
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://sysid2006.org/
BibTex Citekey: 3640
 Art des Abschluß: -

Veranstaltung

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Titel: 14th IFAC Symposium on System Identification (SYSID 2006)
Veranstaltungsort: Newcastle, Australia
Start-/Enddatum: -

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Titel: Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Oxford, United Kingdom : Elsevier
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1 - 6 Identifikator: -