| Title |
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
|
|---|---|
| Published in |
Machine Learning, January 2002
|
| DOI | 10.1023/a:1012489924661 |
| Authors |
Peter Sollich |
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 % |
|---|---|---|
| Japan | 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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| China | 2 | 2% |
| Spain | 2 | 2% |
| Italy | 1 | <1% |
| Czechia | 1 | <1% |
| Denmark | 1 | <1% |
| Turkey | 1 | <1% |
| Germany | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 107 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 31 | 26% |
| Researcher | 26 | 22% |
| Student > Master | 11 | 9% |
| Student > Bachelor | 9 | 8% |
| Student > Doctoral Student | 8 | 7% |
| Other | 20 | 17% |
| Unknown | 15 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 51 | 43% |
| Engineering | 25 | 21% |
| Mathematics | 7 | 6% |
| Agricultural and Biological Sciences | 4 | 3% |
| Chemical Engineering | 3 | 3% |
| Other | 9 | 8% |
| Unknown | 21 | 18% |
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 09 May 2018.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from Machine Learning
#1,079
of 1,225 outputs
Outputs of similar age
#126,182
of 130,776 outputs
Outputs of similar age from Machine Learning
#7
of 8 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,225 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 1st percentile – i.e., 1% 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 130,776 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.