Jeffrey C. Schlimmer

2.1k citations
20 papers · 1.2k indexed · h-index 10

Jeffrey C. Schlimmer

20 papers receiving 1.0k citations

Peers

Jeffrey C. Schlimmer
Comparison fields: 5 of 78
  • Artificial Intelligence 966
  • Signal Processing 159
  • Information Systems 280
  • Management Science and Operations Research 138
  • Computer Networks and Communications 160
Replace Pietro Torasso with:
Pietro Torasso Italy
Rana Forsati Iran
Stuart H. Rubin United States
Setsuo Ohsuga Japan
Erik Sandewall Sweden
Atsuhiro Takasu Japan
Hongyun Cai Singapore
Justin A. Boyan United States
Ion Muslea United States
Jiun‐Long Huang Taiwan
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Citations per field
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Citations per year

Countries citing papers authored by Jeffrey C. Schlimmer

Since Specialization
Citations

This map shows the geographic impact of Jeffrey C. Schlimmer'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 Jeffrey C. Schlimmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeffrey C. Schlimmer more than expected).

Fields of papers citing papers by Jeffrey C. Schlimmer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey C. Schlimmer. 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 Jeffrey C. Schlimmer. The network helps show where Jeffrey C. Schlimmer may publish in the future.

Co-authorship network

The 6 scholars most cited alongside Jeffrey C. Schlimmer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jeffrey C. Schlimmer Line = papers co-authored together Jeffrey C. Schlimmer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20027
2 19969
3 199425
4
A cost-sensitive machine learning method for the approach and recognize task
19932
5
Using learned dependencies to automatically construct sufficient and sensible editing views
19939
6 19931
7 199345
8 19931
9
Learning meta knowledge for database checking
19918
10
Paradigms for machine learning
19911
11 19913
12
Justification-Based Refinement of Expert Knowledge.
19912
13
Two case studies in cost-sensitive concept acquisition
199036
14
Learning and representation change
198724
15
Concept acquisition through representational adjustment
1987119
16
Beyond incremental processing: tracking concept drift
1986140
17
A case study of incremental concept induction
1986184
18
A note on correlational measures
19862
19 1986262
20 1986287

About Jeffrey C. Schlimmer

Jeffrey C. Schlimmer is a scholar working on Artificial Intelligence, Information Systems, Computer Science Applications, Human-Computer Interaction and Management Science and Operations Research, having authored 20 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (5 papers), Semantic Web and Ontologies (5 papers), AI-based Problem Solving and Planning (5 papers), Data Mining Algorithms and Applications (4 papers), Machine Learning and Algorithms (3 papers), Advanced Database Systems and Queries (2 papers), Fault Detection and Control Systems (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (966 citations), Signal Processing (159 citations), Information Systems (280 citations), Management Science and Operations Research (138 citations) and Computer Networks and Communications (160 citations). Jeffrey C. Schlimmer has collaborated with scholars based in United States. Frequent co-authors include Richard Granger, Douglas Fisher, Ming Tan, John McDermott, T. M. Mitchell and Pat Langley. Their work appears in journals such as Journal of Artificial Intelligence Research, Machine Learning, Robotics and Autonomous Systems, IEEE Transactions on Education and MIT Press eBooks.

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.

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