Demo Data from VLDB 2026 / reviewers in the wild / expert
Y. X. Gu
dblp:63/9703
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1983
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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.
| Artificial intelligence
1 paper |
Image recognition and object detection · 87% Kernel, tree and ensemble methods · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection
character recognition |
0.0 | 1 | 1983 | Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983 |
Computer vision › Image recognition and object detection › character recognition
chinese character recognition |
0.0 | 1 | 1983 | Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983 |
Machine learning › Kernel, tree and ensemble methods › classifier combination
multi-stage classification |
0.0 | 1 | 1983 | Application of a Multilayer Decision Tree in Computer Recognition of Chinese Characters · IEEE Trans. Pattern Anal. Mach. Intell. 1983 |
Methods — techniques the papers use, named apart from their topics
walsh coefficients · 0.0multilayer decision tree · 0.0character alignment · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1983 | Application of a Multilayer Decision Tree in Computer Recognition of Chinese CharactersabstractA multistage classifier with general tree structure has been developed to recognize a large number of Chinese characters. A simple and efficient method of classifying the characters was achieved by choosing the best feature at each stage of the tree. The features used are Walsh coefficients obtained from two profiles of a character projected onto the X-Y orthogonal axes. Some algorithms for aligning the characters were compared and one of them was adopted in this recognition scheme. A high recognition rate of about 99.5 percent was obtained in an experiment with more than 3000 different Chinese characters. Y. X. Gu, Qing Ren Wang, Ching Y. Suen |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |