Hisashi Kashima

1.9k total citations
28 papers, 1.0k citations indexed

About

Hisashi Kashima is a scholar working on Artificial Intelligence, Computer Science Applications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hisashi Kashima has authored 28 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 8 papers in Computer Science Applications and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hisashi Kashima's work include Mobile Crowdsensing and Crowdsourcing (7 papers), Reinforcement Learning in Robotics (3 papers) and Advanced Graph Neural Networks (3 papers). Hisashi Kashima is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (7 papers), Reinforcement Learning in Robotics (3 papers) and Advanced Graph Neural Networks (3 papers). Hisashi Kashima collaborates with scholars based in Japan, Germany and United States. Hisashi Kashima's co-authors include Koji Tsuda, Akihiro Inokuchi, Masashi Sugiyama, Taiji Suzuki, Paul von Bünau, Shinichi Nakajima, Motoaki Kawanabe, Masamichi Shimosaka, Tsuyoshi Kato and Yoshihiro Yamanishi and has published in prestigious journals such as Bioinformatics, Expert Systems with Applications and BMC Bioinformatics.

In The Last Decade

Hisashi Kashima

27 papers receiving 956 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hisashi Kashima Japan 12 563 234 171 143 113 28 1.0k
Tossapon Boongoen Thailand 18 615 1.1× 286 1.2× 160 0.9× 139 1.0× 64 0.6× 59 994
Bert Huang United States 18 766 1.4× 228 1.0× 85 0.5× 140 1.0× 92 0.8× 50 1.3k
Natthakan Iam-On Thailand 16 629 1.1× 336 1.4× 160 0.9× 153 1.1× 51 0.5× 51 966
Andrea Danyluk United States 11 640 1.1× 277 1.2× 131 0.8× 63 0.4× 58 0.5× 31 1.3k
Nina Mishra United States 18 839 1.5× 105 0.4× 70 0.4× 118 0.8× 134 1.2× 37 1.2k
Shlomo Geva Australia 16 661 1.2× 212 0.9× 75 0.4× 79 0.6× 55 0.5× 155 1.1k
Purnamrita Sarkar United States 15 536 1.0× 134 0.6× 100 0.6× 496 3.5× 50 0.4× 35 1.1k
Hwanjo Yu South Korea 16 731 1.3× 169 0.7× 138 0.8× 172 1.2× 65 0.6× 42 1.1k
Jiaming Xu United States 17 717 1.3× 197 0.8× 40 0.2× 248 1.7× 66 0.6× 69 1.2k
Ricardo B. C. Prudêncio Brazil 19 711 1.3× 113 0.5× 257 1.5× 197 1.4× 256 2.3× 91 1.3k

Countries citing papers authored by Hisashi Kashima

Since Specialization
Citations

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

Fields of papers citing papers by Hisashi Kashima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hisashi Kashima

This figure shows the co-authorship network connecting the top 25 collaborators of Hisashi Kashima. A scholar is included among the top collaborators of Hisashi Kashima 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 Hisashi Kashima. Hisashi Kashima 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.
Kashima, Hisashi, Satoshi Oyama, Hiromi Arai, & Junichiro Mori. (2024). Trustworthy human computation: a survey. Artificial Intelligence Review. 57(12).
2.
Kashima, Hisashi, et al.. (2021). Machine Failure Diagnosis by Combining Software Log and Sensor Data. 1–6. 2 indexed citations
3.
Horiguchi, Yuji, et al.. (2020). Stress Prediction from Head Motion. 488–495. 7 indexed citations
4.
Baba, Yukino, et al.. (2018). Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 3 indexed citations
5.
Baba, Yukino, et al.. (2016). Participation recommendation system for crowdsourcing contests. Expert Systems with Applications. 58. 174–183. 20 indexed citations
6.
Nohara, Yasunobu, Partha Pratim Ghosh, Ashir Ahmed, et al.. (2015). Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study. Journal of Medical Internet Research. 17(1). e2–e2. 24 indexed citations
7.
Oyama, Satoshi, et al.. (2015). From one star to three stars: Upgrading legacy open data using crowdsourcing. Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University). 1–9. 3 indexed citations
8.
Baba, Yukino, Hisashi Kashima, Yasunobu Nohara, et al.. (2015). Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries. 1681–1690. 3 indexed citations
9.
Shimosaka, Masamichi, et al.. (2014). Steered crowdsensing. 691–701. 92 indexed citations
10.
Oyama, Satoshi, Kohei Hayashi, & Hisashi Kashima. (2011). Cross-Temporal Link Prediction. Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University). 1188–1193. 30 indexed citations
11.
Tomioka, Ryota, Taiji Suzuki, Masashi Sugiyama, & Hisashi Kashima. (2010). A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. International Conference on Machine Learning. 1087–1094. 15 indexed citations
12.
Saigo, Hiroto, Masahiro Hattori, Hisashi Kashima, & Koji Tsuda. (2010). Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism. BMC Bioinformatics. 11(S1). S31–S31. 11 indexed citations
13.
Teramoto, Reiji & Hisashi Kashima. (2010). Prediction of protein–ligand binding affinities using multiple instance learning. Journal of Molecular Graphics and Modelling. 29(3). 492–497. 10 indexed citations
14.
Sugiyama, Masashi, et al.. (2010). Nonparametric Return Distribution Approximation for Reinforcement Learning. Tokyo Tech Research Repository (Tokyo Institute of Technology). 799–806. 27 indexed citations
15.
Kato, Tsuyoshi, Hisashi Kashima, Masashi Sugiyama, & Kiyoshi Asai. (2009). Conic Programming for Multitask Learning. IEEE Transactions on Knowledge and Data Engineering. 22(7). 957–968. 25 indexed citations
16.
Kashima, Hisashi, Tsuyoshi Kato, Yoshihiro Yamanishi, Masashi Sugiyama, & Koji Tsuda. (2009). Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. 1100–1111. 76 indexed citations
17.
Kashima, Hisashi, Yoshihiro Yamanishi, Tsuyoshi Kato, Masashi Sugiyama, & Koji Tsuda. (2009). Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. Bioinformatics. 25(22). 2962–2968. 18 indexed citations
18.
Sugiyama, Masashi, et al.. (2009). Least absolute policy iteration for robust value function approximation. 4. 2904–2909. 4 indexed citations
19.
Kato, Tsuyoshi, Hisashi Kashima, & Masashi Sugiyama. (2008). Integration of Multiple Networks for Robust Label Propagation. 716–726. 7 indexed citations
20.
Kashima, Hisashi, Koji Tsuda, & Akihiro Inokuchi. (2003). Marginalized kernels between labeled graphs. Max Planck Institute for Plasma Physics. 321–328. 371 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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