The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Machine learning for streaming data: state of the art, challenges, and opportunities
Abstract
Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing multiple passes over data. Several works have been devoted to this area, either directly or indirectly as characteristics of big data processing, i.e., Velocity and Volume. Given the current industry needs, there are many challenges to be addressed before existing methods can be efficiently applied to real-world problems. In this work, we focus on elucidating the connections among the current stateof- the-art on related fields; and clarifying open challenges in both academia and industry. We treat with special care topics that were not thoroughly investigated in past position and survey papers. This work aims to evoke discussion and elucidate the current research opportunities, highlighting the relationship of different subareas and suggesting courses of action when possible.
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Proceedings of the 19th Python in Science Conference
(2020) · Proceedings of the Python in Science Conference · doi:10.25080/majora-342d178e-02b · Cited by 284
Metadata
- Authors
- Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2019-11-26
- Volume
- 21
- Issue
- 2
- Pages
- 6-22
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2997967197
- Cited by
- 267
- References
- 113
- FWCI
- 11.63
- Type
- journal article
- Language
- en
- Open access
- closed
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