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BIGDATA: Analytical Approaches to Massive Data Computation with Applications to Genomics

Web page for the 'BIGDATA: Analytical Approaches to Massive Data Computation with Applications to Genomics' project at Brown University Department of Computer Science

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BIGDATA: Analytical Approaches to Massive Data Computation with Applications to Genomics | bigdata.cs.brown.edu Reviews
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BIGDATA: Analytical Approaches to Massive Data Computation with Applications to Genomics | bigdata.cs.brown.edu Reviews

https://bigdata.cs.brown.edu

Web page for the 'BIGDATA: Analytical Approaches to Massive Data Computation with Applications to Genomics' project at Brown University Department of Computer Science

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VC-Dimension and Rademacher Averages: From Statistical Learning Theory to Sampling Algorithm

http://bigdata.cs.brown.edu/vctutorial

VC-Dimension and Rademacher Averages:. From Statistical Learning Theory to Sampling Algorithms. A Tutorial by Matteo Riondato. Is a natural technique to speed up the execution of algorithms on very large datasets. The results obtained by analyzing only a random sample of the dataset are an. The number of quantities of interest can be extremely large. Classical techniques like the Union bound are insufficient. Rademacher Averages and the Vapnik-Chervonenkis dimension. Have been developed to overcome this ...

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