Scalable online learning for flink: SOLMA library

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Authors: Jamil, W., Duong, N.C., Wang, W., Mansouri, C., Mohamad, S. and Bouchachia, A.

http://eprints.bournemouth.ac.uk/31449/

Journal: ACM International Conference Proceeding Series

ISBN: 9781450364836

DOI: 10.1145/3241403.3241438

© 2018 Association for Computing Machinery. Driven by the needs of Flink to expand the offline engine to a hybrid one, a new machine learning (ML) library, called SOLMA is proposed. This library aims to cover online learning algorithms for data streams. In this setting, data streams are processed sequentially example by example. SOLMA, which is under development, currently contains two classes of algorithms: (i) basic streaming routines such as online sampling, online PCA, online statistical moments and (ii) advanced online ML algorithms covering in particular classification, regression and drift/anomaly detection and handling. This paper briefly highlights the concepts underlying SOLMA.

This data was imported from Web of Science (Lite):

Authors: Jamil, W., Duong, N.-C., Wang, W., Mansouri, C., Mohamad, S., Bouchachia, A. and Machinery, A.C.

http://eprints.bournemouth.ac.uk/31449/

Journal: ECSA 2018: PROCEEDINGS OF THE 12TH EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE: COMPANION PROCEEDINGS

DOI: 10.1145/3241403.3241438

The data on this page was last updated at 04:57 on June 24, 2019.