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dc.contributor.authorGillian, Nicholas
dc.contributor.authorParadiso, Joseph A.
dc.date.accessioned2016-07-18T15:20:24Z
dc.date.available2016-07-18T15:20:24Z
dc.date.issued2014-10
dc.date.submitted2014-05
dc.identifier.issn1533-7928
dc.identifier.issn1532-4435
dc.identifier.urihttp://hdl.handle.net/1721.1/103640
dc.description.abstractThe Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and gesture recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic gesture spotting.en_US
dc.language.isoen_US
dc.publisherMIT Pressen_US
dc.relation.isversionofhttp://jmlr.org/papers/v15/gillian14a.htmlen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceJournal of Machine Learning Researchen_US
dc.titleThe gesture recognition toolkiten_US
dc.typeArticleen_US
dc.identifier.citationGillian, Nicholas, and Joseph A. Paradiso. "The gesture recognition toolkit." Journal of Machine Learning Research 15 (2014), pp.3483-3487.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.mitauthorGillian, Nicholasen_US
dc.contributor.mitauthorParadiso, Joseph A.en_US
dc.relation.journalJournal of Machine Learning Researchen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-0719-7104
mit.licensePUBLISHER_POLICYen_US


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