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Journal of Machine Learning Research

dc.contributor.authorKlanke, Stefan
dc.contributor.authorVijayakumar, Sethu
dc.contributor.authorSchaal, Stefan
dc.date.accessioned2010-08-23T16:29:56Z
dc.date.available2010-08-23T16:29:56Z
dc.date.issued2008
dc.identifier.issn1532-4435en
dc.identifier.urihttp://jmlr.csail.mit.edu/papers/volume9/klanke08a/klanke08a.pdfen
dc.identifier.urihttp://hdl.handle.net/1842/3665
dc.description.abstractIn this paper we introduce an improved implementation of locally weighted projection regression (LWPR), a supervised learning algorithm that is capable of handling high-dimensional input data. As the key features, our code supports multi-threading, is available for multiple platforms, and provides wrappers for several programming languages.en
dc.language.isoenen
dc.subjectregressionen
dc.subjectlocal learningen
dc.subjectonline learningen
dc.subjectCen
dc.subjectMatlaben
dc.subjectC++en
dc.subjectOctaveen
dc.subjectPythonen
dc.titleA Library for Locally Weighted Projection Regressionen
dc.typeArticleen
rps.volume9en
rps.titleJournal of Machine Learning Researchen
dc.extent.pageNumbers623-626en
dc.date.updated2010-08-23T16:29:57Z
dc.identifier.eIssn1533-7928en


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