TY - RPRT U1 - Forschungsbericht A1 - Bartz-Beielstein, Thomas T1 - EASD - Experimental Algorithmics for Streaming Data N2 - 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). T3 - CIplus - 2/2016 KW - Maschinelles Lernen KW - Data Mining KW - Algorithmus KW - Experiment KW - Experimental Algorithmics KW - On-line Algorithm KW - Machine Learning KW - Massive Online Analysis Y2 - 2016 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-3385 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-3385 SP - 18 S1 - 18 ER -