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Semi-Supervised Regression and System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)Report (Other academic)
Abstract [en]

System Identification and Machine Learning are developing mostly as independent subjects, although the underlying problem is the same: To be able to associate “outputs” with “inputs”. Particular areas in machine learning of substantial current interest are manifold learning and unsupervised and semi-supervised regression. We outline a general approach to semi-supervised regression, describe its links to Local Linear Embedding, and illustrate its use for various problems. In particular, we discuss how these techniques have a potential interest for the system identification world.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 21 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2940
Keyword [en]
Semi-supervised regression, System identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97591ISRN: LiTH-ISY-R-2940OAI: oai:DiVA.org:liu-97591DiVA: diva2:649220
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2013-09-17 Created: 2013-09-17 Last updated: 2014-09-22Bibliographically approved

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fulltext(877 kB)238 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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