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Editorial: Mathematical Fundamentals of Machine Learning

Overview of attention for article published in Frontiers in Applied Mathematics and Statistics, April 2021
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users

Citations

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2 Dimensions

Readers on

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1 Mendeley
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Title
Editorial: Mathematical Fundamentals of Machine Learning
Published in
Frontiers in Applied Mathematics and Statistics, April 2021
DOI 10.3389/fams.2021.674785
Authors

David Glickenstein, Keaton Hamm, Xiaoming Huo, Yajun Mei, Martin Stoll

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 100%
Readers by discipline Count As %
Unspecified 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 March 2021.
All research outputs
#18,142,662
of 23,306,612 outputs
Outputs from Frontiers in Applied Mathematics and Statistics
#181
of 347 outputs
Outputs of similar age
#305,140
of 434,205 outputs
Outputs of similar age from Frontiers in Applied Mathematics and Statistics
#8
of 14 outputs
Altmetric has tracked 23,306,612 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 347 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 434,205 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.