Semi-Supervised Regression and System Identification
2010 (English)Report (Other academic)
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.
LiTH-ISY-R, ISSN 1400-3902 ; 2940
Semi-supervised regression, System identiﬁcation
IdentifiersURN: urn:nbn:se:liu:diva-97591ISRN: LiTH-ISY-R-2940OAI: oai:DiVA.org:liu-97591DiVA: diva2:649220
FunderSwedish Foundation for Strategic Research Swedish Research Council