Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67356
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dc.contributor.authorShi, Q.en
dc.contributor.authorPetterson, J.en
dc.contributor.authorDror, G.en
dc.contributor.authorLangford, J.en
dc.contributor.authorSmola, A.en
dc.contributor.authorVishwanathan, S.en
dc.date.issued2009en
dc.identifier.citationJournal of Machine Learning Research (Print), 2009; 10:2615-2637en
dc.identifier.issn1532-4435en
dc.identifier.issn1533-7928en
dc.identifier.urihttp://hdl.handle.net/2440/67356-
dc.description.abstractWe propose hashing to facilitate efficient kernels. This generalizes previous work using sampling and we show a principled way to compute the kernel matrix for data streams and sparse feature spaces. Moreover, we give deviation bounds from the exact kernel matrix. This has applications to estimation on strings and graphsen
dc.description.statementofresponsibilityQinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola and S.V.N. Vishwanathanen
dc.language.isoenen
dc.publisherMIT Pressen
dc.rights© 2009 Authors. Copyright © JMLR 2009. All rights reserved.en
dc.source.urihttp://jmlr.csail.mit.edu/papers/v10/en
dc.subjecthashing, stream, string kernel, graphlet kernel, multiclass classificationen
dc.titleHash Kernels for Structured Dataen
dc.typeJournal articleen
dc.identifier.rmid0020112762en
dc.identifier.pubid27725-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]en
Appears in Collections:Computer Science publications

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