EDBT 2026 Demo / reviewers in the wild / expert
Shao-Bo Wang
dblp:91/2526
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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 |
Learning theory · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › margin-based learning
large margin classification |
0.2 | 1 | 2016 | Graph Quality Judgement: A Large Margin Expedition · IJCAI 2016 |
Methods — techniques the papers use, named apart from their topics
large margin · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Instance selection method for improving graph-based semi-supervised learning
Shao-Bo Wang, Yufeng Li 0008 |
Frontiers Comput. Sci. | 2 |
| 2016 | Graph Quality Judgement: A Large Margin Expedition
Yufeng Li 0008, Shao-Bo Wang, Zhi-Hua Zhou |
IJCAI | 2 |
| 2016 | Instance Selection Method for Improving Graph-Based Semi-supervised Learning
Shao-Bo Wang, Yufeng Li 0008 |
PRICAI | 2 |
| 2007 | H.264 Intra Prediction Architecture OptimizationabstractThis paper proposes an optimized H.264 Intra Prediction arithmetic to reduce the time required to complete the intra prediction by saving ninety percent waste time during the process of intra prediction. Firstly, the 16×16 luminance prediction can be decomposed equally into 16 parts and inserted into the same macro-block's 4×4 luminance prediction to save the 16×16 luminance prediction time in one macro-block's luminance prediction. Then, by means of changing the computation sequence of 4×4 luminance prediction, a large amount of waiting time between 4×4 blocks' luminance predictions can be further removed. Almost all the waste time can be reused or eliminated by these optimizations. At the last part of this paper, an optimized hardware implementation aiming at fitting to the whole H.264 video coding system and a method to realize 16×16 luminance DC prediction on it are proposed. Shao-Bo Wang, Xiaolin Zhang 0002 |
ICME | 1 |