Demo Data from EDBT 2026 / reviewers in the wild / expert
Noah S. Friedland
dblp:06/3832
· DBLP profile ↗
5ranked-venue papers
3as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 3 first-authorDatabases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 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 |
Knowledge representation and reasoning · 92% Image recognition and object detection · 6% Probabilistic and Bayesian machine learning · 2% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
learning by reading |
0.1 | 1 | 2007 | Learning by Reading: A Prototype System, Performance Baseline and Lessons Learned · AAAI 2007 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge-based systems |
0.0 | 1 | 2004 | Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems · KR 2004 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition
knowledge base construction |
0.0 | 1 | 2007 | Learning by Reading: A Prototype System, Performance Baseline and Lessons Learned · AAAI 2007 |
Computer vision › Image recognition and object detection
object recognition |
0.0 | 1 | 1992 | Compact Object Recognition Using Energy-Function-Based Optimization · IEEE Trans. Pattern Anal. Mach. Intell. 1992 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models
markov random field |
0.0 | 1 | 1992 | Compact Object Recognition Using Energy-Function-Based Optimization · IEEE Trans. Pattern Anal. Mach. Intell. 1992 |
Methods — techniques the papers use, named apart from their topics
simulated annealing · 0.0energy function minimization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Learning by Reading: A Prototype System, Performance Baseline and Lessons Learned
Ken Barker 0002, Bhalchandra Agashe, Shaw Yi Chaw, James Fan, Noah S. Friedland, Michael R. Glass, Jerry R. Hobbs, Eduard H. Hovy, David J. Israel, Doo Soon Kim, Rutu Mulkar-Mehta, Sourabh Patwardhan, Bruce W. Porter, Dan Tecuci, Peter Z. Yeh |
AAAI | 5 |
| 2004 | Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems
Noah S. Friedland, Paul G. Allen, Michael Witbrock, Gavin Matthews, Nancy Salay, Pierluigi Miraglia, Jürgen Angele, Steffen Staab, David J. Israel, Vinay K. Chaudhri, Bruce W. Porter, Ken Barker 0002, Peter Clark |
KR | 1 |
| 2003 | Panel discussion on knowledge acquisition projects (Halo, RKF, and AKT)abstractNo abstract available. Nigel Shadbolt, Murray A. Burke, Noah S. Friedland |
K-CAP | 3 |
| 1997 | An integrated approach to 2D object recognition
Noah S. Friedland, Azriel Rosenfeld |
Pattern Recognit. | 1 |
| 1992 | Compact Object Recognition Using Energy-Function-Based OptimizationabstractDescribes a method of recognizing objects whose contours can be represented in smoothly varying polar coordinate form. Both low- and high-level information about the object (contour smoothness and edge sharpness at the low level and contour shape at the high level) are incorporated into a single energy function that defines a 1D, cyclic, Markov random field (1DCMRF). This 1DCMRF is based on a polar coordinate object representation whose center can be initialized at any location within the object. The recognition process is based on energy function minimization, which is implemented by simulated annealing.> Noah S. Friedland, Azriel Rosenfeld |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |