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dc.contributor.authorWitten, Ian H.
dc.date.accessioned2010-02-11T01:32:54Z
dc.date.available2010-02-11T01:32:54Z
dc.date.issued2009
dc.identifier.citationWitten, I. H. (2009). Classfication. In L. Liu & M. T. Ozsu (Eds), Encylopedia of Database Systems. Springer.en
dc.identifier.urihttps://hdl.handle.net/10289/3561
dc.description.abstractIn Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringeren
dc.relation.urihttp://www.springer.com/computer+science/database+management+%26+information+retrieval/book/978-0-387-49616-0
dc.rightsThis is an author’s accepted version of an article published in the book: Encylopedia of Database Systems. ©2009 Springer.en
dc.subjectcomputer scienceen
dc.subjectclassification learningen
dc.subjectsupervised learningen
dc.subjectstatistical decision techniquesen
dc.subjectMachine learning
dc.titleClassificationen
dc.typeChapter in Booken
dc.identifier.doi10.1007/978-0-387-39940-9_552en_NZ
dc.relation.isPartOfEncyclopedia of Database Systemsen_NZ
pubs.begin-page331en_NZ
pubs.elements-id9143
pubs.end-page335en_NZ
pubs.place-of-publicationUSen_NZ


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