Journal article
Signature methods in machine learning
- Abstract:
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Signature-based techniques give mathematical insight into the interactions between complex streams of evolving data. These insights can be quite naturally translated into numerical approaches to understanding streamed data, and perhaps because of their mathematical precision, have proved useful in analysing streamed data in situations where the data is irregular, and not stationary, and the dimension of the data and the sample sizes are both moderate. Understanding streamed multi-modal data i...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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-
(Preview, Accepted manuscript, pdf, 3.8MB, Terms of use)
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- Publisher copy:
- 10.4171/emss/95
Authors
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/S026347/1
- Publisher:
- EMS Press
- Journal:
- EMS Surveys in Mathematical Sciences More from this journal
- Publication date:
- 2025-02-19
- Acceptance date:
- 2025-01-19
- DOI:
- EISSN:
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2308-216X
- ISSN:
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2308-2151
- Language:
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English
- Keywords:
- Pubs id:
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2080363
- Local pid:
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pubs:2080363
- Deposit date:
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2025-01-24
Terms of use
- Copyright holder:
- European Mathematical Society
- Copyright date:
- 2025
- Rights statement:
- © 2025 European Mathematical Society
- Notes:
-
This is the accepted manuscript version of the article. The final version is available online from EMS Press at https://dx.doi.org/10.4171/emss/95
The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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