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Statements

Subject Item
n2:90115
rdf:type
bibo:AcademicArticle n14:EPrint bibo:Article n14:ArticleEPrint
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n5:TFUZZ.2017.2769039
dcterms:title
Autonomous Learning Multimodel Systems From Data Streams
wdrs:describedby
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dcterms:date
2018-08-01
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n12:ext-x.gu@kent.ac.uk n12:ext-945f95bab9a79005d133178ebe758b24 n12:ext-835461cfa2b64c2beaf2be7d687b8dbb
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bibo: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.
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bibo:issue
4
bibo:volume
26