Countries citing papers authored by Tomoharu Nakashima
Since
Specialization
Citations
This map shows the geographic impact of Tomoharu Nakashima'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 Tomoharu Nakashima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomoharu Nakashima more than expected).
Fields of papers citing papers by Tomoharu Nakashima
This network shows the impact of papers produced by Tomoharu Nakashima. 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 Tomoharu Nakashima. The network helps show where Tomoharu Nakashima may publish in the future.
Co-authorship network of co-authors of Tomoharu Nakashima
This figure shows the co-authorship network connecting the top 25 collaborators of Tomoharu Nakashima.
A scholar is included among the top collaborators of Tomoharu Nakashima 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 Tomoharu Nakashima. Tomoharu Nakashima is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nakashima, Tomoharu, et al.. (2012). Incremental learning of fuzzy rule-based classifiers for large data sets. World Automation Congress. 1–5.1 indexed citations
4.
Nakashima, Tomoharu, et al.. (2010). Off-line learning of soccer formations from game logs. World Automation Congress. 1–6.2 indexed citations
5.
Nakashima, Tomoharu, et al.. (2008). Cost-sensitive techniques for fuzzy rule-based pattern classification. World Automation Congress. 1–6.1 indexed citations
6.
Nakashima, Tomoharu, et al.. (2007). Relation between the Performance of Ensemble Classification Systems and the Diversity of Classification Systems. 한국지능시스템학회 국제학술대회 발표논문집. 320–323.1 indexed citations
Nakashima, Tomoharu, et al.. (2005). Learning Fuzzy If-Then Rules for Pattern Classi cation with Weighted Training Patterns.. European Society for Fuzzy Logic and Technology Conference. 1064–1069.7 indexed citations
9.
Ishibuchi, Hisao, Tomoharu Nakashima, & Manabu Nii. (2004). Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining (Advanced Information Processing). Springer eBooks.123 indexed citations
Nakashima, Tomoharu, et al.. (2002). A Boosting Algorithm of Fuzzy Rule-Based Systems for Pattern Classification Problems.. 155–158.2 indexed citations
15.
Nakashima, Tomoharu, et al.. (2002). On-Line Learning of a Fuzzy System for a Future Market.. 54–58.2 indexed citations
Ishibuchi, Hisao & Tomoharu Nakashima. (2000). Linguistic rule extraction by genetics-based machine learning. Genetic and Evolutionary Computation Conference. 195–202.11 indexed citations
18.
Ishibuchi, Hisao, et al.. (2000). Evolution of strategies in spatial IPD games with structured demes. Genetic and Evolutionary Computation Conference. 817–824.1 indexed citations
19.
Ishibuchi, Hisao & Tomoharu Nakashima. (2000). Multi-objective pattern and feature selection by a genetic algorithm. Genetic and Evolutionary Computation Conference. 1069–1076.32 indexed citations
20.
Nakashima, Tomoharu, et al.. (1998). Performance Evaluation of Fuzzy Q-Learning. Transactions of the Institute of Electronics, Information and Communication Engineers. 81(1). 194–197.1 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.