Masamichi Shimura

632 total citations
28 papers, 261 citations indexed

About

Masamichi Shimura is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Masamichi Shimura has authored 28 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Computer Networks and Communications. Recurrent topics in Masamichi Shimura's work include Neural Networks and Applications (7 papers), AI-based Problem Solving and Planning (6 papers) and Bayesian Methods and Mixture Models (2 papers). Masamichi Shimura is often cited by papers focused on Neural Networks and Applications (7 papers), AI-based Problem Solving and Planning (6 papers) and Bayesian Methods and Mixture Models (2 papers). Masamichi Shimura collaborates with scholars based in Japan, United Kingdom and Philippines. Masamichi Shimura's co-authors include J. Nagumo, Masayuki Numao, Einoshin Suzuki, Riichiro Mizoguchi, Boonserm Kijsirikul, C.V. Negoita, L. A. Zadeh, Kai Fu, Kazuo Tanaka and Kambiz Badie and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Artificial Intelligence.

In The Last Decade

Masamichi Shimura

25 papers receiving 241 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Masamichi Shimura Japan 9 118 56 43 42 33 28 261
Jiří Vomlel Czechia 10 242 2.1× 37 0.7× 53 1.2× 35 0.8× 32 1.0× 30 335
Roberto Torres United States 7 121 1.0× 163 2.9× 36 0.8× 28 0.7× 9 0.3× 10 281
Maria Florina Balcan United States 10 210 1.8× 17 0.3× 33 0.8× 40 1.0× 15 0.5× 17 361
Darius Braziunas Canada 10 167 1.4× 167 3.0× 106 2.5× 23 0.5× 22 0.7× 10 347
J. E. L. Peck Canada 10 179 1.5× 33 0.6× 16 0.4× 136 3.2× 12 0.4× 33 436
Simone Romano Australia 9 259 2.2× 32 0.6× 19 0.4× 29 0.7× 10 0.3× 11 381
M. J. R. Shave United Kingdom 6 127 1.1× 42 0.8× 10 0.2× 64 1.5× 8 0.2× 22 314
Christopher Musco United States 11 222 1.9× 41 0.7× 34 0.8× 66 1.6× 12 0.4× 26 415
Michael Yu Zhu United States 3 509 4.3× 141 2.5× 47 1.1× 40 1.0× 10 0.3× 7 580
Finnegan Southey Canada 12 196 1.7× 11 0.2× 28 0.7× 43 1.0× 7 0.2× 18 290

Countries citing papers authored by Masamichi Shimura

Since Specialization
Citations

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

Fields of papers citing papers by Masamichi Shimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masamichi Shimura. 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 Masamichi Shimura. The network helps show where Masamichi Shimura may publish in the future.

Co-authorship network of co-authors of Masamichi Shimura

This figure shows the co-authorship network connecting the top 25 collaborators of Masamichi Shimura. A scholar is included among the top collaborators of Masamichi Shimura based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Masamichi Shimura. Masamichi Shimura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Numao, Masayuki, et al.. (2000). Multistrategy Discovery and Detection of Novice Programmer Errors. Machine Learning. 38(1-2). 157–180. 24 indexed citations
2.
Shimura, Masamichi, et al.. (1998). Discovering Error Classes from Discrepancies in Novice Behaviors Via Multistrategy Conceptual Clustering. User Modeling and User-Adapted Interaction. 8(1-2). 103–129. 10 indexed citations
3.
Numao, Masayuki, et al.. (1997). Using data and theory in multistrategy (mis) concept (ion) discovery. International Joint Conference on Artificial Intelligence. 274–279. 3 indexed citations
4.
Murata, Tsuyoshi & Masamichi Shimura. (1996). Machine discovery based on numerical data generated in computer experiments. National Conference on Artificial Intelligence. 41(7). 737–742. 1 indexed citations
5.
Suzuki, Einoshin & Masamichi Shimura. (1996). Exceptional knowledge discovery in databases based on information theory. Knowledge Discovery and Data Mining. 275–278. 23 indexed citations
6.
Murata, Tsuyoshi, Masami Mizutani, & Masamichi Shimura. (1994). A discovery system for trigonometric functions. National Conference on Artificial Intelligence. 645–650. 4 indexed citations
7.
Shimura, Masamichi, et al.. (1993). Learning from an approximate theory and noisy examples. National Conference on Artificial Intelligence. 466–471. 2 indexed citations
8.
Kijsirikul, Boonserm, Masayuki Numao, & Masamichi Shimura. (1992). Discrimination-based constructive induction of logic programs. National Conference on Artificial Intelligence. 42(8). 44–49. 20 indexed citations
9.
Shimura, Masamichi, et al.. (1990). Parametric engineering design using constraint-based reasoning. National Conference on Artificial Intelligence. 505–510. 6 indexed citations
10.
Shimura, Masamichi, et al.. (1986). Learning arithmetic problem solver. National Conference on Artificial Intelligence. 1036–1040.
11.
Badie, Kambiz & Masamichi Shimura. (1982). Machine Recognition of Arabic Handprinted Scripts. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 107–114. 4 indexed citations
12.
Mizoguchi, Riichiro & Masamichi Shimura. (1980). A Nonparametric Algorithm for Detecting Clusters Using Hierarchical Structure. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-2(4). 292–300. 17 indexed citations
13.
Shimura, Masamichi, et al.. (1976). Learning with probabilistic labeling. Pattern Recognition. 8(1). 5–10. 4 indexed citations
14.
Shimura, Masamichi, et al.. (1975). An Approach to Unsupervised Learning Classification. IEEE Transactions on Computers. C-24(10). 979–983. 6 indexed citations
15.
Shimura, Masamichi. (1973). Fuzzy sets concept in rank-ordering objects. Journal of Mathematical Analysis and Applications. 43(3). 717–733. 38 indexed citations
16.
Shimura, Masamichi. (1973). Recognizing machines with parametric and nonparametric learning methods using contextual information. Pattern Recognition. 5(2). 149–168. 3 indexed citations
17.
Shimura, Masamichi, et al.. (1973). General consideration of four-layer series-coupled machines with learning mechanisms. Kybernetik. 13(2). 95–103. 1 indexed citations
18.
Shimura, Masamichi, et al.. (1973). Rule-oriented methods in problem solving. Artificial Intelligence. 4(3-4). 203–223. 2 indexed citations
19.
Shimura, Masamichi, et al.. (1973). Nonsupervised classification using the principal component. Pattern Recognition. 5(4). 353–363. 7 indexed citations
20.
Shimura, Masamichi. (1973). Multicategory Learning Classifiers for Character Reading. IEEE Transactions on Systems Man and Cybernetics. SMC-3(1). 74–85. 5 indexed citations

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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