Charles Elkan
Impact in
- Artificial Intelligence top 0.1%
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Signal Processing top 0.5%
Papers in
-
- Machine Learning and Data Classification 16
- Machine Learning and Algorithms 13
- Imbalanced Data Classification Techniques 12
- AI-based Problem Solving and Planning 10
- Text and Document Classification Technologies 10
- Logic, Reasoning, and Knowledge 9
- Topic Modeling 9
- Co-authors
- Trisha L. BaileyBianca ZadroznyZachary C. LiptonJohn BerkowitzGreg HamerlyTimothy L. BaileyKeith NotoAlvaro Monge
- Journals
- Machine Learning (6 papers)Artificial Intelligence (3 papers)Biochemical and Biophysical Research Communications (2 papers)Computer applications in the biosciences (2 papers)Ecography (1 paper)
- Partner nations
- United StatesCanadaHungary
In The Last Decade
Charles Elkan
95 papers receiving 13.8k citations
Hit Papers
Peers
Comparison fields: 5 of 221
- Artificial Intelligence 5.7k
- Signal Processing 1.1k
- Computer Vision and Pattern Recognition 1.6k
- Information Systems 1.6k
- Molecular Biology 4.9k
Countries citing papers authored by Charles Elkan
This map shows the geographic impact of Charles Elkan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Charles Elkan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Charles Elkan more than expected).
Fields of papers citing papers by Charles Elkan
This network shows the impact of papers produced by Charles Elkan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Charles Elkan. The network helps show where Charles Elkan may publish in the future.
Co-authors
The 25 scholars most cited alongside Charles Elkan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Predicting Surgery Duration with Neural Heteroscedastic Regression | 2017 | 1 |
| 2 | F1-Optimal Thresholding in the Multi-Label Setting. | 2014 | 0 |
| 3 | Optimal Thresholding of Classifiers to Maximize F1 Measure Hit paper breakdown → | 2014 | 361 |
| 4 | Conditional Random Fields for Word Hyphenation | 2010 | 5 |
| 5 | Sources of Success for Boosted Wrapper Induction | 2004 | 7 |
| 6 | Principled methods for advising reinforcement learning agents | 2003 | 84 |
| 7 | Learning the k in k-means | 2003 | 419 |
| 8 | 2003 | 8 | |
| 9 | Shared challenges in data mining and computational biology | 2001 | 1 |
| 10 | KDD'99 Knowledge Discovery Contest. | 2000 | 2 |
| 11 | 1997 | 32 | |
| 12 | The field matching problem: Algorithms and applications | 1996 | 297 |
| 13 | Integrating External Information Sources to Guide Worldwide Web Information Retrieval | 1995 | 3 |
| 14 | 1994 | 9 | |
| 15 | The paradoxical success of fuzzy logic | 1993 | 44 |
| 16 | Estimating the accuracy of learned concepts | 1993 | 23 |
| 17 | Measuring and improving the effectiveness of representations | 1991 | 8 |
| 18 | Incremental, approximate planning | 1990 | 20 |
| 19 | Conspiracy numbers and caching for searching and/or trees and theorem-proving | 1989 | 18 |
| 20 | Automated Inductive Reasoning about Logic Programs. | 1988 | 6 |
About Charles Elkan
Charles Elkan is a scholar working on Artificial Intelligence, Computational Mathematics, Information Systems, Computer Vision and Pattern Recognition and Signal Processing, having authored 97 papers that have together received 14.6k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (16 papers), Machine Learning and Algorithms (13 papers), Imbalanced Data Classification Techniques (12 papers), AI-based Problem Solving and Planning (10 papers), Text and Document Classification Technologies (10 papers), Logic, Reasoning, and Knowledge (9 papers), Topic Modeling (9 papers) and Machine Learning in Bioinformatics (7 papers). The work is most often cited by research in Artificial Intelligence (5.7k citations), Signal Processing (1.1k citations), Computer Vision and Pattern Recognition (1.6k citations), Information Systems (1.6k citations) and Molecular Biology (4.9k citations). Charles Elkan has collaborated with scholars based in United States, Canada and Hungary. Frequent co-authors include Trisha L. Bailey, Bianca Zadrozny, Zachary C. Lipton, John Berkowitz, Greg Hamerly, Timothy L. Bailey, Keith Noto, Alvaro Monge, William Noble Grundy and Michael E. Baker. Their work appears in journals such as Machine Learning, Artificial Intelligence, Biochemical and Biophysical Research Communications, Computer applications in the biosciences and Ecography.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.