Demo Data from EDBT 2026 / reviewers in the wild / expert
Zoltán Györfi
dblp:50/1948
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 1991
—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
1 paper |
Coding theory · 100% | |
| Artificial intelligence
1 paper |
Learning theory · 75% Kernel, tree and ensemble methods · 25% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › source coding › rate-distortion theory
fidelity criterion |
0.0 | 1 | 1991 | On the Gaarder-Slepion 'tracking system' conjecture · IEEE Trans. Inf. Theory 1991 |
Coding theory › source coding › source modeling
markov sources |
0.0 | 1 | 1991 | On the Gaarder-Slepion 'tracking system' conjecture · IEEE Trans. Inf. Theory 1991 |
Coding theory
source coding |
0.0 | 1 | 1991 | On the Gaarder-Slepion 'tracking system' conjecture · IEEE Trans. Inf. Theory 1991 |
Machine learning › Learning theory › generalization error
asymptotic error probability |
0.0 | 1 | 1978 | An upper bound on the asymptotic error probability on the k-nearest neighbor rule for multiple classes (Corresp.) · IEEE Trans. Inf. Theory 1978 |
Machine learning › Learning theory › classification › classification theory
bayes error rate |
0.0 | 1 | 1978 | An upper bound on the asymptotic error probability on the k-nearest neighbor rule for multiple classes (Corresp.) · IEEE Trans. Inf. Theory 1978 |
Machine learning › Kernel, tree and ensemble methods
nearest neighbor methods |
0.0 | 1 | 1978 | An upper bound on the asymptotic error probability on the k-nearest neighbor rule for multiple classes (Corresp.) · IEEE Trans. Inf. Theory 1978 |
Machine learning › Learning theory
statistical learning theory |
0.0 | 1 | 1978 | An upper bound on the asymptotic error probability on the k-nearest neighbor rule for multiple classes (Corresp.) · IEEE Trans. Inf. Theory 1978 |
Methods — techniques the papers use, named apart from their topics
randomization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | On the Gaarder-Slepion 'tracking system' conjectureabstractThe authors examine under what conditions, and with what notion of optimality, the coder of an optimal system can be operated with the information which is also accessible to the decoder. After a discussion of the difficulties involved, a theorem is proved for Markov sources which states that the extra memory of the coder can be substituted with independent randomization. The result is not tied to any specific notion of optimality. The main result is shown to hold for Markov sources of order k under weak assumptions on the structure and with respect to any per-letter fidelity criterion.> George Gabor, Gábor Szekeres, Zoltán Györfi |
IEEE Trans. Inf. Theory | 3 |
| 1978 | An upper bound on the asymptotic error probability on the k-nearest neighbor rule for multiple classes (Corresp.)abstractIfR_{k}, denotes the asymptotic error probability of thek- nearest neighbor rule forMclasses andR\astdenotes the Bayes probability of error, then conditions are given that yieldR_{k} - R\ast \leq \sqrt{MR{1}/k}. László Györfi, Zoltán Györfi |
IEEE Trans. Inf. Theory | 2 |