| Title |
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
|
|---|---|
| Published in |
Machine Learning, April 1988
|
| DOI | 10.1007/bf00116827 |
| Authors |
Nick Littlestone |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 3 | 2% |
| United States | 3 | 2% |
| China | 3 | 2% |
| Italy | 2 | 1% |
| France | 2 | 1% |
| Germany | 2 | 1% |
| Canada | 2 | 1% |
| United Kingdom | 2 | 1% |
| Switzerland | 1 | <1% |
| Other | 7 | 5% |
| Unknown | 108 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 59 | 44% |
| Researcher | 24 | 18% |
| Student > Master | 17 | 13% |
| Other | 8 | 6% |
| Student > Bachelor | 6 | 4% |
| Other | 18 | 13% |
| Unknown | 3 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 100 | 74% |
| Engineering | 10 | 7% |
| Linguistics | 4 | 3% |
| Economics, Econometrics and Finance | 2 | 1% |
| Physics and Astronomy | 2 | 1% |
| Other | 6 | 4% |
| Unknown | 11 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 23 November 2022.
All research outputs
#5,410,691
of 26,017,215 outputs
Outputs from Machine Learning
#157
of 1,266 outputs
Outputs of similar age
#1,571
of 12,814 outputs
Outputs of similar age from Machine Learning
#2
of 5 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,266 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 87% 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 12,814 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 87% of its contemporaries.
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.