Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
In The Last Decade
doi.org/w7974854 →Countries where authors are citing Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
This map shows the geographic impact of Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. 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 Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples more than expected).
Fields of papers citing Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
This network shows the impact of Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples.
About Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
This paper, published in 2006, received 2.3k indexed citations . Written by Mikhail Belkin, Partha Niyogi and Vikas Sindhwani covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (1.3k citations), Computer Vision and Pattern Recognition (1.3k citations) and Media Technology (279 citations). Published in Journal of Machine Learning Research.
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This paper is also available at doi.org/w7974854.