Robert E. Schapire
Impact in
- Ecological Modeling top 0.01%
- Species Distribution and Climate Change
- Artificial Intelligence top 0.01%
- Machine Learning and Algorithms
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Neural Networks and Applications
Papers in
-
- Machine Learning and Algorithms 105
- Machine Learning and Data Classification 38
- Algorithms and Data Compression 29
- Imbalanced Data Classification Techniques 23
- Neural Networks and Applications 15
-
- Advanced Bandit Algorithms Research 35
- Co-authors
- Yoav Freund (26 shared papers)Steven J. Phillips (5 shared papers)Robert P. Anderson (2 shared papers)Yoram Singer (14 shared papers)Miroslav Dudı́k (8 shared papers)Peter L. Bartlett (2 shared papers)Wei Chu (2 shared papers)Mary E. Blair (1 shared paper)
- Journals
- Machine Learning (19 papers)Journal of Machine Learning Research (7 papers)Information and Computation (3 papers)SIAM Journal on Computing (3 papers)Journal of Computer and System Sciences (3 papers)
- Partner nations
- United StatesIsraelItaly
In The Last Decade
Robert E. Schapire
170 papers receiving 75.7k citations
Robert E. Schapire's Hit Papers
Peers
Comparison fields: 5 of 233
- Ecological Modeling 16.0k
- Artificial Intelligence 31.5k
- Computer Vision and Pattern Recognition 15.3k
- Nature and Landscape Conservation 7.9k
- Ecology 12.0k
Countries citing papers authored by Robert E. Schapire
This map shows the geographic impact of Robert E. Schapire'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 Robert E. Schapire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert E. Schapire more than expected).
Fields of papers citing papers by Robert E. Schapire
This network shows the impact of papers produced by Robert E. Schapire. 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 Robert E. Schapire. The network helps show where Robert E. Schapire may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert E. Schapire, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 173 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting Hit paper breakdown → | 1997 | 15202 |
| 2 | Maximum entropy modeling of species geographic distributions Hit paper breakdown → | 2005 | 14326 |
| 3 | Novel methods improve prediction of species’ distributions from occurrence data Hit paper breakdown → | 2006 | 7584 |
| 4 | Experiments with a new boosting algorithm Hit paper breakdown → | 1996 | 5921 |
| 5 | The Strength of Weak Learnability Hit paper breakdown → | 1990 | 2509 |
| 6 | A Short Introduction to Boosting Hit paper breakdown → | 1999 | 2206 |
| 7 | Opening the black box: an open‐source release of Maxent Hit paper breakdown → | 2017 | 2167 |
| 8 | Improved boosting algorithms using confidence-rated predictions Hit paper breakdown → | 1998 | 2107 |
| 9 | The strength of weak learnability Hit paper breakdown → | 1990 | 2048 |
| 10 | A desicion-theoretic generalization of on-line learning and an application to boosting Hit paper breakdown → | 1995 | 1835 |
| 11 | Boosting the margin: a new explanation for the effectiveness of voting methods Hit paper breakdown → | 1998 | 1790 |
| 12 | BoosTexter: A Boosting-based System for Text Categorization Hit paper breakdown → | 2000 | 1764 |
| 13 | Improved Boosting Algorithms Using Confidence-rated Predictions Hit paper breakdown → | 1999 | 1740 |
| 14 | A contextual-bandit approach to personalized news article recommendation Hit paper breakdown → | 2010 | 1508 |
| 15 | The Boosting Approach to Machine Learning: An Overview Hit paper breakdown → | 2003 | 1501 |
| 16 | The Nonstochastic Multiarmed Bandit Problem Hit paper breakdown → | 2002 | 1258 |
| 17 | A brief introduction to boosting Hit paper breakdown → | 1999 | 912 |
| 18 | Large margin classification using the perceptron algorithm Hit paper breakdown → | 1998 | 857 |
| 19 | Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers Hit paper breakdown → | 2000 | 783 |
| 20 | Explaining AdaBoost Hit paper breakdown → | 2013 | 755 |
About Robert E. Schapire
Robert E. Schapire is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computational Theory and Mathematics, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 173 papers that have together received 80.1k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (105 papers), Machine Learning and Data Classification (38 papers), Advanced Bandit Algorithms Research (35 papers), Algorithms and Data Compression (29 papers), Imbalanced Data Classification Techniques (23 papers), Optimization and Search Problems (15 papers), Neural Networks and Applications (15 papers) and Face and Expression Recognition (14 papers). The work is most often cited by research in Ecological Modeling (16.0k citations), Artificial Intelligence (31.5k citations), Computer Vision and Pattern Recognition (15.3k citations), Nature and Landscape Conservation (7.9k citations) and Ecology (12.0k citations). Robert E. Schapire has collaborated with scholars based in United States, Israel and Italy. Frequent co-authors include Yoav Freund, Steven J. Phillips, Robert P. Anderson, Yoram Singer, Miroslav Dudı́k, Peter L. Bartlett, Wei Chu, Mary E. Blair, Nicolò Cesa‐Bianchi and John C. Langford. Their work appears in journals such as Machine Learning, Journal of Machine Learning Research, Information and Computation, SIAM Journal on Computing and Journal of Computer and System Sciences.
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.