Takayoshi Shoudai

582 total citations
32 papers, 91 citations indexed

About

Takayoshi Shoudai is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Takayoshi Shoudai has authored 32 papers receiving a total of 91 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 15 papers in Computational Theory and Mathematics and 8 papers in Information Systems. Recurrent topics in Takayoshi Shoudai's work include Algorithms and Data Compression (18 papers), Machine Learning and Algorithms (14 papers) and semigroups and automata theory (10 papers). Takayoshi Shoudai is often cited by papers focused on Algorithms and Data Compression (18 papers), Machine Learning and Algorithms (14 papers) and semigroups and automata theory (10 papers). Takayoshi Shoudai collaborates with scholars based in Japan. Takayoshi Shoudai's co-authors include Tomoyuki Uchida, Satoru Miyano, Yusuke Suzuki, Tetsuhiro Miyahara, Tsunenori Mine, Satoshi Matsumoto, Yuta Suzuki, Takashi Yamada, Takashi Uchida and Ayumi Shinohara and has published in prestigious journals such as Machine Learning, Theoretical Computer Science and Information Processing Letters.

In The Last Decade

Takayoshi Shoudai

27 papers receiving 90 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Takayoshi Shoudai Japan	6	69	41	17	10	10	32	91
Hiroki Shizuya Japan	5	99 1.4×	38 0.9×	22 1.3×	28 2.8×	11 1.1×	26	112
Hans de Nivelle Germany	5	121 1.8×	66 1.6×	12 0.7×	19 1.9×	4 0.4×	16	127
Lewis M. Norton United States	9	153 2.2×	26 0.6×	17 1.0×	18 1.8×	33 3.3×	25	174
Rachid Echahed France	6	79 1.1×	66 1.6×	10 0.6×	10 1.0×	4 0.4×	27	99
Ludovic Perret France	6	101 1.5×	51 1.2×	29 1.7×	10 1.0×	6 0.6×	14	111
Aurélien Lemay France	6	80 1.2×	56 1.4×	20 1.2×	14 1.4×	3 0.3×	12	94
Peggy Cellier France	6	41 0.6×	30 0.7×	30 1.8×	8 0.8×	11 1.1×	18	82
Wim Vanhoof Belgium	7	54 0.8×	45 1.1×	23 1.4×	13 1.3×	8 0.8×	20	108
Duhyeong Kim South Korea	6	101 1.5×	20 0.5×	34 2.0×	14 1.4×	6 0.6×	10	124
Satoshi Obana Japan	5	44 0.6×	11 0.3×	18 1.1×	13 1.3×	6 0.6×	17	57

Countries citing papers authored by Takayoshi Shoudai

Since Specialization

Citations

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

Fields of papers citing papers by Takayoshi Shoudai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takayoshi Shoudai

This figure shows the co-authorship network connecting the top 25 collaborators of Takayoshi Shoudai. A scholar is included among the top collaborators of Takayoshi Shoudai 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 Takayoshi Shoudai. Takayoshi Shoudai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Uchida, Tomoyuki, Satoshi Matsumoto, Takayoshi Shoudai, Yusuke Suzuki, & Tetsuhiro Miyahara. (2019). Exact Learning of Primitive Formal Systems Defining Labeled Ordered Tree Languages via Queries. IEICE Transactions on Information and Systems. E102.D(3). 470–482.

Suzuki, Yusuke, Takayoshi Shoudai, Tomoyuki Uchida, & Tetsuhiro Miyahara. (2015). An Efficient Pattern Matching Algorithm for Ordered Term Tree Patterns. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. E98.A(6). 1197–1211. 2 indexed citations

Shoudai, Takayoshi, et al.. (2013). Structure-based Data Mining and Screening for Network Traffic Data. 58. 152–157. 3 indexed citations

Yamada, Takashi & Takayoshi Shoudai. (2011). Efficient Pattern Matching on Graph Patterns of Bounded Treewidth. Electronic Notes in Discrete Mathematics. 37. 117–122. 1 indexed citations

Suzuki, Yusuke, et al.. (2010). Learning Characteristic Structured Patterns in Rooted Planar Maps. International MultiConference of Engineers and Computer Scientists. 2180(1). 465–470.

Shoudai, Takayoshi, et al.. (2009). Learning block-preserving graph patterns and its application to data mining. Machine Learning. 76(1). 137–173. 11 indexed citations

Shoudai, Takayoshi, et al.. (2009). A Polynomial Time Algorithm for Finding a Minimally Generalized Linear Interval Graph Pattern. IEICE Transactions on Information and Systems. E92-D(2). 120–129. 2 indexed citations

Suzuki, Yusuke, Takayoshi Shoudai, Tomoyuki Uchida, & Tetsuhiro Miyahara. (2005). Ordered term tree languages which are polynomial time inductively inferable from positive data. Theoretical Computer Science. 350(1). 63–90. 9 indexed citations

Suzuki, Yusuke, Tetsuhiro Miyahara, Takayoshi Shoudai, T Uchida, & Yuichi Nakamura. (2005). Discovery of Maximally Frequent Tag Tree Patterns with Height-Constrained Variables from Semistructured Web Documents. 12. 104–112. 2 indexed citations

10.

Miyahara, Tetsuhiro, Yusuke Suzuki, Takayoshi Shoudai, et al.. (2003). Extraction of tag tree patterns with contractible variables from irregular semistructured data. Knowledge Discovery and Data Mining. 2637. 430–436. 1 indexed citations

11.

Mine, Tsunenori, et al.. (2002). Automatic Generating Appropriate Exercises Based on Dynamic Evaluating both Students' and Questions' Levels. 2002(1). 1898–1903. 2 indexed citations

12.

Mine, Tsunenori, et al.. (2000). Automatic Exercise Generator with Tagged Documents Considering Learner's Performance. World Conference on WWW and Internet. 2000(1). 779–780. 2 indexed citations

13.

Mine, Tsunenori, et al.. (2000). The Design and implementation of Automatic Exercise Generator with Tagged Documents based on the Intelligence of Students : AEGIS. Kyushu University Institutional Repository (QIR) (Kyushu University). 651–658. 5 indexed citations

14.

Shoudai, Takayoshi, Tetsuhiro Miyahara, Tomoyuki Uchida, & Satoshi Matsumoto. (1999). Inductive Inference of Regular Term Free Languages and Its Application to Knowledge Discovery.. European Journal of Combinatorics. 85–102. 2 indexed citations

15.

Noda, Kiyoshi, Satoshi Matsumoto, Ayumi Shinohara, Takayoshi Shoudai, & Satoru Miyano. (1997). Gene Finding Using HAKKE System. Proceedings Genome Informatics Workshop/Genome informatics. 8. 318–319. 1 indexed citations

16.

Matsumoto, Satoshi, et al.. (1996). HAKKE: A Multi-Strategy Prediction System for Sequences. Proceedings Genome Informatics Workshop/Genome informatics. 7. 98–107. 2 indexed citations

17.

Uchida, Tomoyuki, Takayoshi Shoudai, & Satoru Miyano. (1995). Parallel Algorithms for Refutation Tree Problem on Formal Graph Systems. IEICE Transactions on Information and Systems. 59(2). 99–112. 7 indexed citations

18.

Shoudai, Takayoshi & Satoru Miyano. (1995). Using maximal independent sets to solve problems in parallel. Theoretical Computer Science. 148(1). 57–65. 1 indexed citations

19.

Shoudai, Takayoshi, Michael Lappé, Satoru Miyano, et al.. (1995). BONSAI Garden: parallel knowledge discovery system for amino acid sequences.. PubMed. 3. 359–66. 2 indexed citations

20.

Shoudai, Takayoshi, et al.. (1991). Using Maximal Independent Sets to Solve Problems in Parallel. Kyushu University Institutional Repository (QIR) (Kyushu University). 2 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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