The Case against Accuracy Estimation for Comparing Induction Algorithms
- Journal
- International Conference on Machine Learning
In The Last Decade
doi.org/w7495100 →Countries where authors are citing The Case against Accuracy Estimation for Comparing Induction Algorithms
This map shows the geographic impact of The Case against Accuracy Estimation for Comparing Induction Algorithms. 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 The Case against Accuracy Estimation for Comparing Induction Algorithms with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites The Case against Accuracy Estimation for Comparing Induction Algorithms more than expected).
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This network shows the impact of The Case against Accuracy Estimation for Comparing Induction Algorithms. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the The Case against Accuracy Estimation for Comparing Induction Algorithms.
About The Case against Accuracy Estimation for Comparing Induction Algorithms
This paper, published in 1998, received 715 indexed citations . Written by Foster Provost, Tom Fawcett and Ron Kohavi covering the research area of Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. It is primarily cited by scholars working on Artificial Intelligence (452 citations), Information Systems (188 citations) and Computer Vision and Pattern Recognition (77 citations). Published in International Conference on Machine Learning.
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This paper is also available at doi.org/w7495100.