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DC poleHodnotaJazyk
dc.contributor.authorHast, Anders
dc.contributor.authorVats, Ekta
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-30T08:55:46Z
dc.date.available2021-08-30T08:55:46Z
dc.date.issued2021
dc.identifier.citationJournal of WSCG. 2021, vol. 29, no. 1-2, p. 39-47.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2021/2021-J-WSCG-1-2.pdf
dc.identifier.urihttp://hdl.handle.net/11025/44947
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectpodprostorycs
dc.subjectvestavěné prototypycs
dc.subjectclusteringcs
dc.subjecthluboké učenícs
dc.subjectsamoorganizující se mapycs
dc.subjectt-SNEcs
dc.subjectrozdělení datcs
dc.titleWord Recognition using Embedded Prototype Subspace Classifiers on a New Imbalanced Dataseten
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper presents an approach towards word recognition based on embedded prototype subspace classification.The purpose of this paper is three-fold. Firstly, a new dataset for word recognition is presented, which is extractedfrom the Esposalles database consisting of the Barcelona cathedral marriage records. Secondly, different clusteringtechniques are evaluated for Embedded Prototype Subspace Classifiers. The dataset, containing 30 different classesof words is heavily imbalanced, and some word classes are very similar, which renders the classification task ratherchallenging. For ease of use, no stratified sampling is done in advance, and the impact of different data splits isevaluated for different clustering techniques. It will be demonstrated that the original clustering technique based onscaling the bandwidth has to be adjusted for this new dataset. Thirdly, an algorithm is therefore proposed that findskclusters, striving to obtain a certain amount of feature points in each cluster, rather than finding some clustersbased on scaling the Silverman’s rule of thumb. Furthermore, Self Organising Maps are also evaluated as both aclustering and embedding technique.en
dc.subject.translatedsubspacesen
dc.subject.translatedEmbedded Prototypesen
dc.subject.translatedclusteringen
dc.subject.translateddeep learningen
dc.subject.translatedSelf Organising Mapsen
dc.subject.translatedt-SNEen
dc.subject.translatedData splitsen
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2021.29.5
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 29, Number 1-2 (2021)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 29 (2021)
  6. Volume 29, Number 1-2 (2021)