Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines
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This map shows the geographic impact of Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines. 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 Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines more than expected).
Fields of papers citing Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines
This network shows the impact of Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines.
About Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines
This paper, published in 1998, received 1.9k indexed citations . Written by John Platt covering the research area of Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. It is primarily cited by scholars working on Artificial Intelligence (887 citations), Computer Vision and Pattern Recognition (577 citations), Information Systems (232 citations), Signal Processing (231 citations) and Control and Systems Engineering (181 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.
This paper is also available at doi.org/w319404.