Demo Data from SIGIR 2026 / reviewers in the wild / expert
F. C. S. Poon
dblp:38/3961
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
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.
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 50% Learning theory · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › statistical pattern recognition
bayes error bound |
0.0 | 1 | 1989 | Comments on Approximating Discrete Probability Distributions with Dependence Trees · IEEE Trans. Pattern Anal. Mach. Intell. 1989 |
Methods — techniques the papers use, named apart from their topics
product approximation · 0.0dependence tree · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | Comments on Approximating Discrete Probability Distributions with Dependence TreesabstractC.K. Chow and C.N. Liu (1968) introduced the notion of three dependence to approximate a kth-order probability distribution. More recently, A.K.C. Wong and C.C. Wang (1977) proposed a different product approximation. The present authors show that the tree dependence approximation suggested by Chow and Liu can be derived by minimizing an upper bound of the Bayes error rate under certain assumptions. They also show that the method proposed by Wong and Wang does not necessarily lead to fewer misclassifications, because it is a special case of such a minimization procedure.> S. K. Michael Wong, F. C. S. Poon |
IEEE Trans. Pattern Anal. Mach. Intell. | 2 |