VLDB 2026 Research / reviewers in the wild / expert
Daniel G. Keehn
dblp:75/2475
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1965
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Information theory · 56% Mathematical optimization · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › estimation theory
bayesian estimation |
0.0 | 1 | 1965 | A note on learning for Gaussian properties · IEEE Trans. Inf. Theory 1965 |
Mathematical optimization
statistical learning |
0.0 | 1 | 1965 | A note on learning for Gaussian properties · IEEE Trans. Inf. Theory 1965 |
Information theory › probability theory › continuous distributions
gaussian distribution |
0.0 | 1 | 1965 | A note on learning for Gaussian properties · IEEE Trans. Inf. Theory 1965 |
Methods — techniques the papers use, named apart from their topics
likelihood computation · 0.0bayesian approach · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1965 | A note on learning for Gaussian propertiesabstractBy employing a Bayesian approach to the analysis of learning the probability distribution of property vectors, an estimation likelihood computation scheme for the general Gaussian distribution (quadratic adaptive decision surface) is shown optimum. Some results relating the number of learning samples to Type I misclassification errors are included. Daniel G. Keehn |
IEEE Trans. Inf. Theory | 1 |