Demo Data from EDBT 2026 / reviewers in the wild / expert
Hans-Martin Wallmeier
dblp:08/1188
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 1985
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 2
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
2 papers |
Information theory · 52% Coding theory · 48% | |
| Artificial intelligence
1 paper |
Learning theory · 100% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › network information theory
multiple-access channel |
0.0 | 2 | 1985 | Random coding bound and codes produced by permutations for the multiple-access channel · IEEE Trans. Inf. Theory 1985 A note on the capacity region of the multiple-access channel (Corresp.) · IEEE Trans. Inf. Theory 1979 |
Coding theory › error-correcting codes › combinatorial coding theory
permutation codes |
0.0 | 1 | 1985 | Random coding bound and codes produced by permutations for the multiple-access channel · IEEE Trans. Inf. Theory 1985 |
Coding theory › channel coding › error probability bounds
random coding bound |
0.0 | 1 | 1985 | Random coding bound and codes produced by permutations for the multiple-access channel · IEEE Trans. Inf. Theory 1985 |
Information theory › channel capacity
capacity region |
0.0 | 1 | 1979 | A note on the capacity region of the multiple-access channel (Corresp.) · IEEE Trans. Inf. Theory 1979 |
Information theory
channel capacity |
0.0 | 1 | 1985 | Random coding bound and codes produced by permutations for the multiple-access channel · IEEE Trans. Inf. Theory 1985 |
Machine learning › Learning theory
classification |
0.0 | 1 | 1979 | A note on the capacity region of the multiple-access channel (Corresp.) · IEEE Trans. Inf. Theory 1979 |
Machine learning › Learning theory › statistical estimation › decision-theoretic estimation
compound decision theory |
0.0 | 1 | 1979 | A note on the capacity region of the multiple-access channel (Corresp.) · IEEE Trans. Inf. Theory 1979 |
Methods — techniques the papers use, named apart from their topics
random coding · 0.0permutation coding · 0.0maximum likelihood · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1985 | Random coding bound and codes produced by permutations for the multiple-access channelabstractA random coding bound for the multiple-access channel (MAC) is given, and it is shown to be obtainable universally for all MAC's with common input and output alphabets. For channels for which the random coding bound may be achieved by codes consisting of codewords of a single type for each sender, this bound is also achievable by codes produced by permutations. Jutta Pokorny, Hans-Martin Wallmeier |
IEEE Trans. Inf. Theory | 2 |
| 1979 | A note on the capacity region of the multiple-access channel (Corresp.)abstractIn the classification problem for chromosomes there areNchromosomes which must be classified intokpopulationsA_(1}, \cdots ,A_{k}having known probability distributions. It is further known that theseNchromosomes haveN_{i}in classA_{i}, i = 1,2, \cdots ,k. This is a compound decision problem whose optimal solution gives a classification algorithm which is not currently useful in practice because of its long computation time. Two other classification methods are considered, and the results are compared. One is the method often used for the classification of the 46 human chromosomes, where the knowledge about the exact number of chromosome types is disregarded, and only the a priori probability that a chromosome originates from populationA_{i}is used. The other method permits only classifications with the correct number of objects in each class and selects from all the possible classifications that one which has the maximum likelihood function. This last method has some advantages for a small number of objects, particularly if the numbers of objects in the classes are equal. Monika Bierbaum, Hans-Martin Wallmeier |
IEEE Trans. Inf. Theory | 2 |