Demo Data from EDBT 2026 / reviewers in the wild / expert
Volkan Vural
dblp:91/4354
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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
3 papers |
Learning theory · 47% Kernel, tree and ensemble methods · 40% Image recognition and object detection · 13% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › classification
multiclass classification |
0.1 | 2 | 2009 | Multi-Class Classifiers and their Underlying Shared Structure · IJCAI 2009 A hierarchical method for multi-class support vector machines · ICML 2004 |
Machine learning › Kernel, tree and ensemble methods › large margin methods
maximum margin classifiers |
0.1 | 1 | 2009 | Using Local Dependencies within Batches to Improve Large Margin Classifiers · J. Mach. Learn. Res. 2009 |
Computer vision › Image recognition and object detection › image classification
hierarchical classification |
0.0 | 1 | 2004 | A hierarchical method for multi-class support vector machines · ICML 2004 |
Machine learning › Kernel, tree and ensemble methods
support vector machine |
0.0 | 1 | 2004 | A hierarchical method for multi-class support vector machines · ICML 2004 |
Machine learning › Learning theory
batch learning |
0.0 | 1 | 2009 | Using Local Dependencies within Batches to Improve Large Margin Classifiers · J. Mach. Learn. Res. 2009 |
Methods — techniques the papers use, named apart from their topics
multi-class classifier · 0.1local dependencies · 0.1large margin classifier · 0.1one-against-rest · 0.0one-against-one · 0.0divide-by-2 · 0.0binary decision tree · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Multi-Class Classifiers and their Underlying Shared Structure
Volkan Vural, Glenn Fung, Rómer Rosales, Jennifer G. Dy |
IJCAI | 1 |
| 2009 | Using Local Dependencies within Batches to Improve Large Margin Classifiers
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao |
J. Mach. Learn. Res. | 1 |
| 2006 | Batch Classification with Applications in Computer Aided Diagnosis
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao |
ECML | 1 |
| 2004 | A hierarchical method for multi-class support vector machinesabstractWe introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class prob-lems. DB2 offers an alternative to the stan-dard one-against-one and one-against-rest al-gorithms. For an N class problem, DB2 pro-duces an N − 1 node binary decision tree where nodes represent decision boundaries formed by N−1 SVM binary classifiers. This tree structure allows us to present a gener-alization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods. 1. Volkan Vural, Jennifer G. Dy |
ICML | 1 |