Tom M. Mitchell
Impact in
- Artificial Intelligence top 0.01%
- Topic Modeling
- Natural Language Processing Techniques
- Machine Learning and Algorithms
- Text and Document Classification Technologies
- AI-based Problem Solving and Planning
- Machine Learning and Data Classification
- Semantic Web and Ontologies
- Domain Adaptation and Few-Shot Learning
- Health Informatics top 0.2%
Papers in
-
- Topic Modeling 64
- Natural Language Processing Techniques 52
- Machine Learning and Algorithms 30
- AI-based Problem Solving and Planning 27
- Semantic Web and Ontologies 19
- Neural Networks and Applications 15
-
- Neurobiology of Language and Bilingualism 14
- Functional Brain Connectivity Studies 13
- Co-authors
- Michael I. Jordan (1 shared paper)Avrim Blum (2 shared papers)Sebastian Thrun (9 shared papers)Kamal Nigam (4 shared papers)Andrew Kachites McCallum (1 shared paper)Smadar T. Kedar-Cabelli (3 shared papers)Richard M. Keller (2 shared papers)Erik Brynjolfsson (4 shared papers)
- Journals
- Machine Learning (7 papers)NeuroImage (5 papers)Science (5 papers)PLoS ONE (4 papers)AI Magazine (4 papers)
- Partner nations
- United StatesUnited KingdomIndia
In The Last Decade
Tom M. Mitchell
234 papers receiving 31.8k citations
Tom M. Mitchell's Hit Papers
Peers
Comparison fields: 5 of 237
- Artificial Intelligence 18.8k
- Health Informatics 328
- Computational Mathematics 140
- Computer Vision and Pattern Recognition 4.7k
- Information Systems 4.6k
Countries citing papers authored by Tom M. Mitchell
This map shows the geographic impact of Tom M. Mitchell'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 Tom M. Mitchell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom M. Mitchell more than expected).
Fields of papers citing papers by Tom M. Mitchell
This network shows the impact of papers produced by Tom M. Mitchell. 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 Tom M. Mitchell. The network helps show where Tom M. Mitchell may publish in the future.
Co-authors
The 25 scholars most cited alongside Tom M. Mitchell, 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 241 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Machine learning: Trends, perspectives, and prospects Hit paper breakdown → | 2015 | 6813 |
| 2 | Combining labeled and unlabeled data with co-training Hit paper breakdown → | 1998 | 4261 |
| 3 | Text Classification from Labeled and Unlabeled Documents using EM Hit paper breakdown → | 2000 | 2210 |
| 4 | Global Analysis of Protein Activities Using Proteome Chips Hit paper breakdown → | 2001 | 1609 |
| 5 | Toward an Architecture for Never-Ending Language Learning Hit paper breakdown → | 2010 | 1397 |
| 6 | Machine learning classifiers and fMRI: A tutorial overview Hit paper breakdown → | 2008 | 1313 |
| 7 | Generalization as search Hit paper breakdown → | 1982 | 1145 |
| 8 | Explanation-Based Generalization: A Unifying View Hit paper breakdown → | 1986 | 973 |
| 9 | Predicting Human Brain Activity Associated with the Meanings of Nouns Hit paper breakdown → | 2008 | 865 |
| 10 | What can machine learning do? Workforce implications Hit paper breakdown → | 2017 | 704 |
| 11 | Explanation-based generalization: A unifying view Hit paper breakdown → | 1986 | 605 |
| 12 | Web Watcher: A Tour Guide for the World Wide Web. Hit paper breakdown → | 1997 | 536 |
| 13 | Machine learning and data mining Hit paper breakdown → | 1999 | 531 |
| 14 | 2000 | 364 | |
| 15 | What Can Machines Learn and What Does It Mean for Occupations and the Economy? Hit paper breakdown → | 2018 | 355 |
| 16 | Zero-Shot Learning with Semantic Output Codes Hit paper breakdown → | 2018 | 332 |
| 17 | 1995 | 320 | |
| 18 | 1994 | 318 | |
| 19 | Coupled semi-supervised learning for information extraction Hit paper breakdown → | 2010 | 316 |
| 20 | 1983 | 295 |
About Tom M. Mitchell
Tom M. Mitchell is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Information Systems, Computer Vision and Pattern Recognition and Developmental and Educational Psychology, having authored 241 papers that have together received 34.3k indexed citations. Recurring topics across this work include Topic Modeling (64 papers), Natural Language Processing Techniques (52 papers), Machine Learning and Algorithms (30 papers), AI-based Problem Solving and Planning (27 papers), Semantic Web and Ontologies (19 papers), Neural Networks and Applications (15 papers), Neurobiology of Language and Bilingualism (14 papers) and Functional Brain Connectivity Studies (13 papers). The work is most often cited by research in Artificial Intelligence (18.8k citations), Health Informatics (328 citations), Computational Mathematics (140 citations), Computer Vision and Pattern Recognition (4.7k citations) and Information Systems (4.6k citations). Tom M. Mitchell has collaborated with scholars based in United States, United Kingdom and India. Frequent co-authors include Michael I. Jordan, Avrim Blum, Sebastian Thrun, Kamal Nigam, Andrew Kachites McCallum, Smadar T. Kedar-Cabelli, Richard M. Keller, Erik Brynjolfsson, J. Andrew Carlson and Matthew Botvinick. Their work appears in journals such as Machine Learning, NeuroImage, Science, PLoS ONE and AI Magazine.
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.