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Proc. 7th International Workshop on Self Organizing Maps (WSOM’09)

dc.contributor.authorSteffen, Jan
dc.contributor.authorKlanke, Stefan
dc.contributor.authorVijayakumar, Sethu
dc.contributor.authorRitter, Helge
dc.date.accessioned2010-08-24T09:43:14Z
dc.date.available2010-08-24T09:43:14Z
dc.date.issued2009
dc.identifier.isbn978-3-642-02396-5en
dc.identifier.urihttp://www.springerlink.com/content/k451684040143110/en
dc.identifier.urihttp://hdl.handle.net/1842/3673
dc.description.abstractWe 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.en
dc.language.isoenen
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.titleTowards Semi-supervised Manifold Learning: UKR with Structural Hintsen
dc.typeConference Paperen
dc.identifier.doi10.1007/978-3-642-02397-2_34en
rps.titleProc. 7th International Workshop on Self Organizing Maps (WSOM’09)en
dc.extent.noOfPages8en
dc.date.updated2010-08-24T09:43:15Z


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