R.M. Goodman

3.7k citations
98 papers · 2.8k · h-index 25

Impact in

Papers in

R.M. Goodman

85 papers receiving 2.6k citations

Peers

R.M. Goodman
Comparison fields: 5 of 120
  • Insect Science 488
  • Artificial Intelligence 1.1k
  • Computational Theory and Mathematics 355
  • Computer Networks and Communications 486
  • Computer Vision and Pattern Recognition 433
Replace Dongbing Gu with:
Dongbing Gu United Kingdom
Hyondong Oh South Korea
Gilles Venturini France
Antonio Pedro Aguiar Portugal
Veysel Gazi Türkiye
Andrew B. Howard United States
Gustavo Olague Mexico
Donatello Conte France
Tarun Kumar Sharma India
Michael Lindenbaum Israel
R.M. Goodman relative to Dongbing Gu United Kingdom Dongbing Gu's profile →
Citations per field
00.5×5×10×14.4×
Dongbing Gu · 1×
Citations per year

Countries citing papers authored by R.M. Goodman

Since Specialization
Citations

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

Fields of papers citing papers by R.M. Goodman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by R.M. Goodman. 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 R.M. Goodman. The network helps show where R.M. Goodman may publish in the future.

Co-authors

The 25 scholars most cited alongside R.M. Goodman, 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 R.M. Goodman Line = papers co-authored together R.M. Goodman links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 98 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002385
2 1992267
3 1997253
4
Rule Induction Using Information Theory.
1991179
5 1991121
6 2003107
7 1993107
8 200295
9 199190
10 200271
11 200067
12 199260
13 198858
14 199354
15 199450
16 199450
17 199046
18 198845
19 200241
20 199440

About R.M. Goodman

R.M. Goodman is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Statistical and Nonlinear Physics, having authored 98 papers that have together received 2.8k indexed citations. Recurring topics across this work include Neural Networks and Applications (26 papers), Error Correcting Code Techniques (14 papers), Coding theory and cryptography (10 papers), Algorithms and Data Compression (10 papers), Machine Learning and Algorithms (7 papers), CCD and CMOS Imaging Sensors (7 papers), Advanced Wireless Communication Techniques (7 papers) and Image Retrieval and Classification Techniques (6 papers). The work is most often cited by research in Insect Science (488 citations), Artificial Intelligence (1.1k citations), Computational Theory and Mathematics (355 citations), Computer Networks and Communications (486 citations) and Computer Vision and Pattern Recognition (433 citations). R.M. Goodman has collaborated with scholars based in United States, United Kingdom and Taiwan. Frequent co-authors include Padhraic Smyth, Alcherio Martinoli, Adam T. Hayes, John Kim, Changhoon Lee, Zheng Zeng, Charles M. Higgins, Tzi‐Dar Chiueh, John W. V. Miller and J.T. Coffey. Their work appears in journals such as IEEE Transactions on Information Theory, Neural Computation, IEEE Transactions on Computers, Electronics Letters and IEEE Transactions on Fuzzy Systems.

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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