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Engineering fast multilevel support vector machines

Overview of attention for article published in Machine Learning, May 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
12 Mendeley
Title
Engineering fast multilevel support vector machines
Published in
Machine Learning, May 2019
DOI 10.1007/s10994-019-05800-7
Authors

Ehsan Sadrfaridpour, Talayeh Razzaghi, Ilya Safro

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Researcher 3 25%
Student > Master 2 17%
Unknown 3 25%
Readers by discipline Count As %
Engineering 5 42%
Computer Science 3 25%
Business, Management and Accounting 1 8%
Unknown 3 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 May 2019.
All research outputs
#7,377,704
of 13,770,158 outputs
Outputs from Machine Learning
#291
of 442 outputs
Outputs of similar age
#116,949
of 254,573 outputs
Outputs of similar age from Machine Learning
#2
of 4 outputs
Altmetric has tracked 13,770,158 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 442 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 34th percentile – i.e., 34% 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 254,573 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.