Zobrazit minimální záznam

dc.contributor.advisorKracík, Jan
dc.contributor.authorNguyen, Sinh Xuan
dc.date.accessioned2020-10-02T09:27:56Z
dc.date.available2020-10-02T09:27:56Z
dc.date.issued2020
dc.identifier.otherOSD002
dc.identifier.urihttp://hdl.handle.net/10084/142071
dc.description.abstractSupervised learning is an important problem in machine learning. In this thesis, we focus on probabilistic classification based on linear models and mixture models to find the ones that are the most suitable for the given data.en
dc.description.abstractSupervised learning is an important problem in machine learning. In this thesis, we focus on probabilistic classification based on linear models and mixture models to find the ones that are the most suitable for the given data.cs
dc.format.extent2346935 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherVysoká škola báňská – Technická univerzita Ostravacs
dc.subjectKeywords: probabilistic classification, bayesian statistics, linear model, mixture model.en
dc.subjectKeywords: probabilistic classification, bayesian statistics, linear model, mixture model.cs
dc.titleProbabilistic Classifiersen
dc.title.alternativePravděpodobnostní klasifikátorycs
dc.typeDiplomová prácecs
dc.contributor.refereeDomesová, Simona
dc.date.accepted2020-08-20
dc.thesis.degree-nameIng.
dc.thesis.degree-levelMagisterský studijní programcs
dc.thesis.degree-grantorVysoká škola báňská – Technická univerzita Ostrava. Fakulta elektrotechniky a informatikycs
dc.description.department470 - Katedra aplikované matematikycs
dc.thesis.degree-programInformační a komunikační technologiecs
dc.thesis.degree-branchVýpočetní matematikacs
dc.description.resultdobřecs
dc.identifier.senderS2724
dc.identifier.thesisNGU0099_FEI_N2647_1103T031_2020
dc.rights.accessopenAccess


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Zobrazit minimální záznam