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DC poleHodnotaJazyk
dc.contributor.authorSkorkovská, Lucie
dc.contributor.authorZajíc, Zbyněk
dc.contributor.authorMüller, Luděk
dc.date.accessioned2015-12-17T11:14:34Z
dc.date.available2015-12-17T11:14:34Z
dc.date.issued2014
dc.identifier.citationSKORKOVSKÁ, Lucie; ZAJÍC, Zbyněk; MÜLLER, Luděk. Comparison of score normalization methods applied to multi-label classification. In: IEEE international symposium on signal processing and information technology. Noida: IEEE Press, 2014, p. [1-5].en
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/LucieSkorkovska_2014_ComparisonofScore
dc.identifier.urihttp://hdl.handle.net/11025/17048
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEE Pressen
dc.rights© Lucie Skorkovská - Zbyněk Zajíc - Luděk Müllercs
dc.subjectmulti-label klasifikace textucs
dc.subjectidentifikace tématucs
dc.subjectnaivní bayesovská klasifikacecs
dc.subjectnormalizace skórecs
dc.titleComparison of score normalization methods applied to multi-label classificationen
dc.title.alternativePorovnání metod pro normalizaci skóre aplikovaných na úlohu multi-label klasifikacecs
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedOur paper deals with the multi-label text classification of the newspaper articles, where the classifier must decide if a document does or does not belong to each topic from the predefined topic set. A generative classifier is used to tackle this task and the problem with finding a threshold for the positive classification is mainly addressed. This threshold can vary for each document depending on the content of the document (words used, length of the document, etc.). An extensive comparison of the score normalization methods, primary proposed in the speaker identification/verification task, for robustly finding the threshold defining the boundary between the "correct'' and the "incorrect'' topics of a document is presented. Score normalization methods (based on World Model and Unconstrained Cohort Normalization) applied to the topic identification task has shown an improvement of results in our former experiments, therefore in this paper an in-depth experiments with more score normalization techniques applied to the multi-label classification were performed. Thorough analysis of the effects of the various parameters setting is presented.en
dc.subject.translatedmulti-label text classificationen
dc.subject.translatedtopic identificationen
dc.subject.translatednaive bayes classificationen
dc.subject.translatedscore normalizationen
dc.type.statusPeer-revieweden
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