Edinburgh Research Archive

Towards Semi-supervised Manifold Learning: UKR with Structural Hints

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dc.contributor.author
Steffen, Jan
en
dc.contributor.author
Klanke, Stefan
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dc.contributor.author
Vijayakumar, Sethu
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dc.contributor.author
Ritter, Helge
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dc.date.accessioned
2010-08-24T09:43:14Z
dc.date.available
2010-08-24T09:43:14Z
dc.date.issued
2009
dc.date.updated
2010-08-24T09:43:15Z
dc.description.abstract
We explore generic mechanisms to introduce structural hints into the method of Unsupervised Kernel Regression (UKR) in order to learn representations of data sequences in a semi-supervised way. These new extensions are targeted at representing a dextrous manipulation task. We thus evaluate the effectiveness of the proposed mechanisms on appropriate toy data that mimic the characteristics of the aimed manipulation task and thereby provide means for a systematic evaluation.
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dc.extent.noOfPages
8
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dc.identifier.doi
10.1007/978-3-642-02397-2_34
dc.identifier.isbn
978-3-642-02396-5
dc.identifier.uri
http://www.springerlink.com/content/k451684040143110/
dc.identifier.uri
http://hdl.handle.net/1842/3673
dc.language.iso
en
dc.subject
Informatics
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dc.subject
Computer Science
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dc.title
Towards Semi-supervised Manifold Learning: UKR with Structural Hints
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dc.type
Conference Paper
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rps.title
Proc. 7th International Workshop on Self Organizing Maps (WSOM’09)
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