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.
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
199712.7k citationsYoav Freund, Robert E. SchapireJournal of Computer and System Sciencesprofile →
This map shows the geographic impact of Yoav Freund'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 Yoav Freund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoav Freund more than expected).
This network shows the impact of papers produced by Yoav Freund. 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 Yoav Freund. The network helps show where Yoav Freund may publish in the future.
Co-authorship network of co-authors of Yoav Freund
This figure shows the co-authorship network connecting the top 25 collaborators of Yoav Freund.
A scholar is included among the top collaborators of Yoav Freund based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Yoav Freund. Yoav Freund is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Balsubramani, Akshay, Sanjoy Dasgupta, Yoav Freund, & Shay Moran. (2019). An adaptive nearest neighbor rule for classification. eScholarship (California Digital Library). 32. 7577–7586.1 indexed citations
2.
Freund, Yoav, et al.. (2019). Faster Boosting with Smaller Memory. eScholarship (California Digital Library). 32. 11371–11380.1 indexed citations
Freund, Yoav & Robert E. Schapire. (2008). Response to Mease and Wyner, Evidence Contrary to the Statistical View of Boosting, JMLR 9:131-156, 2008. Journal of Machine Learning Research. 9. 171–174.2 indexed citations
8.
Freund, Yoav, Sanjoy Dasgupta, Mayank Kabra, & Nakul Verma. (2007). Learning the structure of manifolds using random projections. Neural Information Processing Systems. 20. 473–480.70 indexed citations
Freund, Yoav, Yishay Mansour, & Robert E. Schapire. (2001). Why averaging classifiers can protect against overfitting.. International Conference on Artificial Intelligence and Statistics. 98–105.21 indexed citations
Schapire, Robert E., et al.. (1997). Boosting the margin: A new explanation for the effectiveness of voting methods. QUT ePrints (Queensland University of Technology). 322–330.395 indexed citations
14.
Freund, Yoav & Robert E. Schapire. (1997). Proceedings of the tenth annual conference on Computational learning theory. Conference on Learning Theory.8 indexed citations
15.
Freund, Yoav & Robert E. Schapire. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences. 55(1). 119–139.12745 indexed citations breakdown →
Freund, Yoav & Yakov Ben‐Haim. (1995). Selectively Sensitive Identification of Connectivity Matrices in Linear Elastic Systems. 2460. 1474.1 indexed citations
18.
Freund, Yoav, H. Sebastian Seung, Eli Shamir, & Naftali Tishby. (1992). Information, Prediction, and Query by Committee. Neural Information Processing Systems. 5. 483–490.56 indexed citations
19.
Freund, Yoav & David Haussler. (1991). Unsupervised learning of distributions on binary vectors using two layer networks. Neural Information Processing Systems. 4. 912–919.173 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
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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.