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EASD - Experimental Algorithmics for Streaming Data

  • This paper proposes an experimental methodology for on-line machine learning algorithms, i.e., for algorithms that work on data that are available in a sequential order. It is demonstrated how established tools from experimental algorithmics (EA) can be applied in the on-line or streaming data setting. The massive on-line analysis (MOA) framework is used to perform the experiments. Benefits of a well-defined report structure are discussed. The application of methods from the EA community to on-line or streaming data is referred to as experimental algorithmics for streaming data (EADS).

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Metadaten
Author:Thomas Bartz-BeielsteinGND
URN:urn:nbn:de:hbz:832-cos4-3385
Series (Serial Number):CIplus (2/2016)
Document Type:Report
Language:English
Year of Completion:2016
Release Date:2016/03/15
Tag:Experimental Algorithmics; Machine Learning; Massive Online Analysis; On-line Algorithm
GND Keyword:Algorithmus; Data Mining; Experiment; Maschinelles Lernen
Pagenumber:18
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:J. Computer Applications
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C10 General
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung