About: Autonomous Learning Multimodel Systems From Data Streams

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90115 Autonomous Learning Multimodel Systems From Data Streams
sameAs	Autonomous Learning Multimodel Systems From Data Streams
Title	Autonomous Learning Multimodel Systems From Data Streams
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-08-01
Creator	Jose C. Principe Plamen P. Angelov Xiaowei Gu
status	peer reviewed published
Publisher	IEEE
abstract	In this paper, an approach to autonomous learning of a multimodel system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types of local models forming multimodel systems. It is fully data driven and its structure is decided by the nonparametric data clouds extracted from the empirically observed data without making any prior assumptions concerning data distribution and other data properties. All metaparameters of the proposed system are obtained directly from the data and can be updated recursively, which improves memory and calculation efficiencies of the proposed algorithm. The structural evolution mechanism and online data cloud quality monitoring mechanism of the ALMMo system largely enhance the ability of handling shifts and/or drifts in the streaming data pattern. Numerical examples of the use of ALMMo system for streaming data analytics, classification, and prediction are presented as a proof of the proposed concept.
Is Part Of	IEEE Transactions on Fuzzy Systems https://kar.kent.ac.uk/id/repository
Subject	QA 75 Electronic computers. Computer science
list of authors	https://kar.kent.ac.uk/id/eprint/90115#authors
issue	4
volume	26
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90115 Autonomous Learning Multimodel Systems From Data Streams

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