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.
This map shows the geographic impact of SingerYoram'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 SingerYoram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SingerYoram more than expected).
This network shows the impact of papers produced by SingerYoram. 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 SingerYoram. The network helps show where SingerYoram may publish in the future.
Co-authorship network
The 1 scholars most cited alongside SingerYoram, linked wherever they have
co-authored with each other. Click a name or a connecting line to browse the papers they
share.
Border = papers with SingerYoramLine = papers co-authored togetherSingerYoram links everyone, so they are left out of the graph.
Journal of Machine Learning Research·優孝 國分,SingerYoram
2003
5
8
On the algorithmic implementation of multiclass kernel-based vector machinesbreakdown →
Journal of Machine Learning Research·優孝 國分,SingerYoram
2002
739
About SingerYoram
SingerYoram is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Industrial and Manufacturing Engineering and Signal Processing, having authored 8 papers that have together received 1.9k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (3 papers), Neural Networks and Applications (1 paper), Distributed Sensor Networks and Detection Algorithms (1 paper), Optimization and Search Problems (1 paper), Machine Learning and Algorithms (1 paper), Metaheuristic Optimization Algorithms Research (1 paper), Machine Learning and ELM (1 paper) and Educational Technology and Assessment (1 paper). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computer Vision and Pattern Recognition (644 citations), Signal Processing (183 citations), Management Science and Operations Research (152 citations) and Information Systems (229 citations). Frequent co-authors include Shalev-ShwartzShai. Their work appears in journals such as Journal of Machine Learning Research and Mathematical Programming.
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.