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1999-01-01Zeitschriftenartikel DOI: 10.18452/9306
Bayesian indexing
dc.contributor.authorSeadle, Michael
dc.date.accessioned2017-06-17T00:40:06Z
dc.date.available2017-06-17T00:40:06Z
dc.date.created2007-10-04
dc.date.issued1999-01-01
dc.date.submitted2007-10-04
dc.identifier.issn0737-8831
dc.identifier.urihttp://edoc.hu-berlin.de/18452/9958
dc.description.abstractBayes’ theorem is about updating assumptions. It provides a mathematical basis for a heuristic search algorithm. A system using such an algorithm could make a kind of “best guess” about what the query is likely to mean. Some disadvantages include the need for more individualized information, a possible tendency to focus on a limited set of works, and the potential for encouraging slopping searching. The literature on heuristic systems is already substantial, and is relevant to all of us in the library and information field.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Philosophische Fakultät I
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectBayesian statisticseng
dc.subjectHeuristicseng
dc.subjectIndexingeng
dc.subject.ddc020 Bibliotheks- und Informationswissenschaften
dc.titleBayesian indexing
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-10080233
dc.identifier.doihttp://dx.doi.org/10.18452/9306
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewed
dc.title.subtitlethe next craze in search algorithms?
dcterms.bibliographicCitation.doi10.1108/07378839910733886
dcterms.bibliographicCitation.journaltitleLibrary Hi Tech
dcterms.bibliographicCitation.volume17
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.pagestart336
dcterms.bibliographicCitation.pageend337
bua.departmentPhilosophische Fakultät I

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