Kernel principal component analysis in data stream mining tasks

Публікація:
Kernel principal component analysis in data stream mining tasks

dc.contributor.author	Bodyanskiy, Ye. V.
dc.contributor.author	Deineko, A. O.
dc.contributor.author	Eze, F. M.
dc.contributor.author	Shalamov, M. O.
dc.date.accessioned	2016-11-09T09:32:50Z
dc.date.available	2016-11-09T09:32:50Z
dc.date.issued	2016
dc.description.abstract	Currently, self-learning systems of computational intelligence [1, 2] and, above all , artificial neural networks (ANN ), that tune their parameters without a teacher on the basis of the self-learning paradigm [3], are widely used in solving various problems of Data Mining, Exploratory Data Analysis etc. Among these tasks, most frequently encountered in the Text Mining, Web Mining, Medical Data Mining, it be can mentioned the problem of compression of large data sets, for whose solution principal component analysis (PCA) is widely used, which consists in the orthogonal projection of input data vectors from the original n-dimensional space in the m- dimensional space of reduced dimensionality	uk_UA
dc.identifier.uri	http://openarchive.nure.ua/handle/document/3434
dc.language.iso	en	uk_UA
dc.subject	data stream	uk_UA
dc.subject	self-learning paradigm	uk_UA
dc.title	Kernel principal component analysis in data stream mining tasks	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication

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