Machine Learning, Neural and Statistical Classification
In The Last Decade
doi.org/10.2307/1269742 →Countries where authors are citing Machine Learning, Neural and Statistical Classification
This map shows the geographic impact of Machine Learning, Neural and Statistical Classification. 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 Machine Learning, Neural and Statistical Classification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Machine Learning, Neural and Statistical Classification more than expected).
Fields of papers citing Machine Learning, Neural and Statistical Classification
This network shows the impact of Machine Learning, Neural and Statistical Classification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Machine Learning, Neural and Statistical Classification.
About Machine Learning, Neural and Statistical Classification
This paper, published in 1995, received 1.4k indexed citations . Written by Bill Fulkerson, D. J. Spiegelhalter and C. C. W. Taylor covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (869 citations), Computer Vision and Pattern Recognition (272 citations) and Information Systems (241 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/10.2307/1269742.