Demo Data from EDBT 2026 / reviewers in the wild / expert
Yong In Han
dblp:99/3020
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1997
—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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › statistical inference › distributed inference
distributed detection |
0.0 | 1 | 1997 | Randomized fusion rules can be optimal in distributed Neyman-Pearson detectors · IEEE Trans. Inf. Theory 1997 |
Information theory › statistical inference › distributed inference
fusion rules |
0.0 | 1 | 1997 | Randomized fusion rules can be optimal in distributed Neyman-Pearson detectors · IEEE Trans. Inf. Theory 1997 |
Information theory › hypothesis testing › binary hypothesis testing
neyman-pearson detection |
0.0 | 1 | 1997 | Randomized fusion rules can be optimal in distributed Neyman-Pearson detectors · IEEE Trans. Inf. Theory 1997 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Randomized fusion rules can be optimal in distributed Neyman-Pearson detectorsabstractWe show that randomized fusion rules can be locally optimal in distributed detection systems under the Neyman-Pearson criterion. This result is contrary to common belief. We first formulate conditions for a randomized fusion rule to be locally optimal. Then, we present distribution functions of local observations that satisfy these conditions. Yong In Han, Taejon Kim |
IEEE Trans. Inf. Theory | 1 |