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).
Author: | Thomas Bartz-BeielsteinGND |
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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: | Maschinelles Lernen; Data Mining; Algorithmus; Experiment |
Page Number: | 18 |
Institutes and Central Facilities: | Fakultät für Informatik und Ingenieurwissenschaften (F10) |
CCS-Classification: | J. Computer Applications |
Dewey Decimal Classification: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
JEL-Classification: | C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C10 General |
Open Access: | Open Access |
Licence (German): | ![]() |