Ensembles and Cascading of Embedded Prototype Subspace Classifiers

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dc.contributor.authorHast, Anders
dc.contributor.authorLind, Mats
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-24T07:07:11Z
dc.date.available2020-07-24T07:07:11Z
dc.date.issued2020
dc.description.abstract-translatedDeep learning approaches suffer from the so called interpretability problem and can therefore be very hard to visualise. Embedded Prototype Subspace Classifiers is one attempt in the field of explainable AI, which is both fast and efficient since it does not require repeated learning epochs and has no hidden layers. In this paper we investigate how ensembles and cascades of ensembles perform on some popular datasets. The focus is on handwritten data such as digits, letters and signs. It is shown how cascading can be efficiently implemented in order to both increase accuracy as well as speed up the classification.en
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationJournal of WSCG. 2020, vol. 28, no. 1-2, p. 89-95.en
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2020.28.11
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38429
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.rights.accessopenAccessen
dc.subjectpodprostorycs
dc.subjectsouborycs
dc.subjectkaskádovánícs
dc.subjectvložené prototypycs
dc.subjectneuronové sítěcs
dc.subjecthluboké učenícs
dc.subject.translatedsubspacesen
dc.subject.translatedensemblesen
dc.subject.translatedcascadingen
dc.subject.translatedembedded prototypesen
dc.subject.translatedneural networksen
dc.subject.translateddeep learningen
dc.titleEnsembles and Cascading of Embedded Prototype Subspace Classifiersen
dc.typečlánekcs
dc.typearticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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Volume 28, Number 1-2 (2020)