Probabilistic Classifiers

Nguyen, Sinh Xuan

Probabilistic Classifiers

dc.contributor.advisor	Kracík, Jan
dc.contributor.author	Nguyen, Sinh Xuan
dc.contributor.referee	Domesová, Simona
dc.date.accepted	2020-08-20
dc.date.accessioned	2020-10-02T09:27:56Z
dc.date.available	2020-10-02T09:27:56Z
dc.date.issued	2020
dc.description.abstract	Supervised 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.abstract	Supervised 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.description.department	470 - Katedra aplikované matematiky	cs
dc.description.result	dobře	cs
dc.format.extent	2346935 bytes
dc.format.mimetype	application/pdf
dc.identifier.other	OSD002
dc.identifier.sender	S2724
dc.identifier.thesis	NGU0099_FEI_N2647_1103T031_2020
dc.identifier.uri	http://hdl.handle.net/10084/142071
dc.language.iso	en
dc.publisher	Vysoká škola báňská – Technická univerzita Ostrava	cs
dc.rights.access	openAccess
dc.subject	Keywords: probabilistic classification, bayesian statistics, linear model, mixture model.	en
dc.subject	Keywords: probabilistic classification, bayesian statistics, linear model, mixture model.	cs
dc.thesis.degree-branch	Výpočetní matematika	cs
dc.thesis.degree-grantor	Vysoká škola báňská – Technická univerzita Ostrava. Fakulta elektrotechniky a informatiky	cs
dc.thesis.degree-level	Magisterský studijní program	cs
dc.thesis.degree-name	Ing.
dc.thesis.degree-program	Informační a komunikační technologie	cs
dc.title	Probabilistic Classifiers	en
dc.title.alternative	Pravděpodobnostní klasifikátory	cs
dc.type	Diplomová práce	cs

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Vysokoškolské kvalifikační práce Fakulty elektrotechniky a informatiky / Theses and dissertations of Faculty of Electrical Engineering and Computer Science (FEI)