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Optimal Rates for the Regularized Learning Algorithms under General Source Condition

Overview of attention for article published in Frontiers in Applied Mathematics and Statistics, March 2017
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3 X users

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Title
Optimal Rates for the Regularized Learning Algorithms under General Source Condition
Published in
Frontiers in Applied Mathematics and Statistics, March 2017
DOI 10.3389/fams.2017.00003
Authors

Abhishake Rastogi, Sivananthan Sampath

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 29%
Researcher 2 29%
Student > Bachelor 1 14%
Unknown 2 29%
Readers by discipline Count As %
Mathematics 3 43%
Computer Science 2 29%
Unknown 2 29%
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 29 March 2017.
All research outputs
#15,448,846
of 22,958,253 outputs
Outputs from Frontiers in Applied Mathematics and Statistics
#143
of 340 outputs
Outputs of similar age
#194,111
of 308,944 outputs
Outputs of similar age from Frontiers in Applied Mathematics and Statistics
#2
of 5 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 340 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 48th percentile – i.e., 48% 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 308,944 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.