SUPPORT VECTOR DENSITY ESTIMATION

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DC Field	Value	Language
dc.contributor.advisor	Boussaad, Abdelmalek	-
dc.contributor.author	Ghedamsi, Oumessaad	-
dc.date.accessioned	2021-10-13T19:33:09Z	-
dc.date.available	2021-10-13T19:33:09Z	-
dc.date.issued	2021	-
dc.identifier.uri	http://dspace.univ-ouargla.dz/jspui/handle/123456789/26588	-
dc.description	Probability and Statistics	-
dc.description.abstract	Our aim in this dissertation is to evoke the area of statistical learning, to define the support vector machines (SVM) as a basic tool in the estimation theory, and to show their ability to estimate a probability density function	en_US
dc.description.abstract	Notre objective dans ce mémoire est d’évoquer le domaine de l’apprentissage statistique, définir les machines à vecteurs de support ou séparateurs à vaste marge (SVM) comme un outil de base dans la théorie d’éstimation et de montrer leur capacité à estimer une densité de probabilité.	-
dc.language.iso	en	en_US
dc.publisher	KASDI MERBAH UNIVERSITY OUARGLA	-
dc.subject	Statistical learning	en_US
dc.subject	Support vector machines (SVM)	en_US
dc.subject	kernel	en_US
dc.subject	density estimation	en_US
dc.title	SUPPORT VECTOR DENSITY ESTIMATION	en_US
dc.type	Thesis	en_US
Appears in Collections:	Département de Mathématiques - Master

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