Shoichi Noda

620 total citations
11 papers, 320 citations indexed

About

Shoichi Noda is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Shoichi Noda has authored 11 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Networks and Communications. Recurrent topics in Shoichi Noda's work include Reinforcement Learning in Robotics (10 papers), Robotic Path Planning Algorithms (8 papers) and Evolutionary Algorithms and Applications (5 papers). Shoichi Noda is often cited by papers focused on Reinforcement Learning in Robotics (10 papers), Robotic Path Planning Algorithms (8 papers) and Evolutionary Algorithms and Applications (5 papers). Shoichi Noda collaborates with scholars based in Japan. Shoichi Noda's co-authors include Minoru Asada, Koh Hosoda, Sukoya Tawaratsumida, Eiji Uchibe and Yasutake Takahashi and has published in prestigious journals such as Machine Learning and Journal of the Robotics Society of Japan.

In The Last Decade

Shoichi Noda

10 papers receiving 278 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shoichi Noda Japan	7	244	96	87	35	35	11	320
Sukoya Tawaratsumida Japan	6	202 0.8×	87 0.9×	71 0.8×	28 0.8×	31 0.9×	7	271
Konstantinos Chatzilygeroudis Greece	10	211 0.9×	61 0.6×	166 1.9×	15 0.4×	94 2.7×	26	384
Paul G. Plöger Germany	10	178 0.7×	64 0.7×	67 0.8×	22 0.6×	19 0.5×	42	344
Nakul Gopalan United States	11	241 1.0×	148 1.5×	113 1.3×	11 0.3×	15 0.4×	23	381
Francisco Cruz Australia	11	247 1.0×	62 0.6×	108 1.2×	8 0.2×	20 0.6×	32	376
Bernd Kleinjohann Germany	10	86 0.4×	112 1.2×	37 0.4×	61 1.7×	9 0.3×	64	331
Ferenc Bálint-Benczédi Germany	10	103 0.4×	118 1.2×	103 1.2×	10 0.3×	18 0.5×	21	246
Adrian Li United States	6	197 0.8×	110 1.1×	119 1.4×	14 0.4×	37 1.1×	9	306
Yoichiro Endo United States	8	80 0.3×	103 1.1×	42 0.5×	75 2.1×	10 0.3×	12	262

Countries citing papers authored by Shoichi Noda

Since Specialization

Citations

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

Fields of papers citing papers by Shoichi Noda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shoichi Noda

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

All Works

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11 of 11 papers shown

1.

Takahashi, Yasutake, Minoru Asada, & Shoichi Noda. (2007). Sensor Space Segmentation for Mobile Robot Learning. 2 indexed citations

2.

Asada, Minoru, Eiji Uchibe, Shoichi Noda, & Sukoya Tawaratsumida. (2004). A Vision-Based Reinforcement Learning For Coordination Of Soccer Playing Behaviors. 6 indexed citations

3.

Uchibe, Eiji, Minoru Asada, Shoichi Noda, & Yasutake Takahashi. (2004). Vision-Based Reinforcement Learning for RoboCup : Towards Real Robot Competition. 3 indexed citations

4.

Asada, Minoru, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (2002). Vision-based behavior acquisition for a shooting robot by using a reinforcement learning. 112–118. 18 indexed citations

5.

Asada, Minoru, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (2002). Vision-based reinforcement learning for purposive behavior acquisition. 1. 146–153. 38 indexed citations

6.

Asada, Minoru, Shoichi Noda, & Koh Hosoda. (2002). Action-based sensor space categorization for robot learning. 3. 1502–1509. 33 indexed citations

7.

Asada, Minoru, Eiji Uchibe, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (2002). Coordination of multiple behaviors acquired by a vision-based reinforcement learning. 2. 917–924. 14 indexed citations

8.

Asada, Minoru, Shoichi Noda, & Koh Hosoda. (1997). Action-Based State Space Construction for Robot Learning.. Journal of the Robotics Society of Japan. 15(6). 886–892. 11 indexed citations

9.

Asada, Minoru, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (1996). Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning. Machine Learning. 23(2-3). 279–303. 3 indexed citations

10.

Asada, Minoru, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (1996). Purposive behavior acquisition for a real robot by vision-based reinforcement learning. Machine Learning. 23(2-3). 279–303. 176 indexed citations

11.

Asada, Minoru, Shoichi Noda, Sukoya Tawaratsumida, & Koh Hosoda. (1995). Learning in robotics. Purposive Behavior Acquisition for a Robot by Vision-Based Reinforcement Learning.. Journal of the Robotics Society of Japan. 13(1). 68–74. 16 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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