Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Discriminative learning for minimum error classification (pattern recognition)
Citations per year, relative to Shigeru Katagiri Shigeru Katagiri (= 1×)
peers
Dong Yu
Countries citing papers authored by Shigeru Katagiri
Since
Specialization
Citations
This map shows the geographic impact of Shigeru Katagiri'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 Shigeru Katagiri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shigeru Katagiri more than expected).
Fields of papers citing papers by Shigeru Katagiri
This network shows the impact of papers produced by Shigeru Katagiri. 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 Shigeru Katagiri. The network helps show where Shigeru Katagiri may publish in the future.
Co-authorship network of co-authors of Shigeru Katagiri
This figure shows the co-authorship network connecting the top 25 collaborators of Shigeru Katagiri.
A scholar is included among the top collaborators of Shigeru Katagiri 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 Shigeru Katagiri. Shigeru Katagiri is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Katagiri, Shigeru, et al.. (2020). Study on Maximum Bayes Boundary-ness Training for Pattern Classification. IEICE Technical Report; IEICE Tech. Rep.. 119(481). 243–248.
3.
Ha, David, et al.. (2018). A Classification-Uncertainty-Based Criterion for Classification Boundary Selection. IEICE Technical Report; IEICE Tech. Rep.. 117(442). 121–126.1 indexed citations
Katagiri, Shigeru, et al.. (2010). Comparison between Minimum Classification Error Training and Support Vector Machine in Prototype-based Classifier Design. IEICE Technical Report; IEICE Tech. Rep.. 110(330). 107–112.
8.
Katagiri, Shigeru, et al.. (2010). Large Geometric Margin Minimum Classification Error Training for Kernel-based High Dimensional Space. IEICE Technical Report; IEICE Tech. Rep.. 110(330). 55–60.1 indexed citations
9.
Katagiri, Shigeru, et al.. (2010). Minimum Classification Error Training with Automatic Control of Loss Smoothness. IEICE technical report. Speech. 110(187). 179–184.2 indexed citations
Haykin, S., James Lo, Craig Fancourt, José C. Prı́ncipe, & Shigeru Katagiri. (2001). Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives. Medical Entomology and Zoology.68 indexed citations
15.
Katagiri, Shigeru, et al.. (1997). Subspace Method for Minimum Error Pattern Recognition. IEICE Transactions on Information and Systems. 80(12). 1195–1204.3 indexed citations
16.
Komori, Takashi & Shigeru Katagiri. (1995). A Minimum Error Approach to Spotting-Based Pattern Recognition. IEICE Transactions on Information and Systems. 78(8). 1032–1043.6 indexed citations
17.
McDermott, Erik, et al.. (1995). A Telephone-based recognition system adaptively trained using Minimum Classification Error/Generalized Probablistic Descent(MCE/GPD). 1995(1). 87–88.1 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.