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Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension

Overview of attention for article published in Machine Learning, January 1994
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

patent
1 patent
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
79 Dimensions

Readers on

mendeley
46 Mendeley
Title
Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
Published in
Machine Learning, January 1994
DOI 10.1007/bf00993163
Authors

David Haussler, Michael Kearns, Robert E. Schapire

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 4%
India 2 4%
United States 2 4%
Czechia 1 2%
Denmark 1 2%
Slovakia 1 2%
Spain 1 2%
Philippines 1 2%
Unknown 35 76%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 10 22%
Student > Master 7 15%
Other 5 11%
Professor > Associate Professor 3 7%
Other 7 15%
Unknown 2 4%
Readers by discipline Count As %
Computer Science 27 59%
Mathematics 3 7%
Engineering 3 7%
Psychology 3 7%
Agricultural and Biological Sciences 1 2%
Other 4 9%
Unknown 5 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 08 March 2024.
All research outputs
#4,773,469
of 23,088,369 outputs
Outputs from Machine Learning
#133
of 978 outputs
Outputs of similar age
#5,565
of 71,546 outputs
Outputs of similar age from Machine Learning
#2
of 6 outputs
Altmetric has tracked 23,088,369 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 978 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 82% of its peers.
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 71,546 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.