Two Applications of PDEs in Data Science
2022-05
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Two Applications of PDEs in Data Science
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2022-05
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In this thesis we explore two applications of partial differential equations to data science. In Part I we establish a convergence rate for the continuum limit of the nondominated sorting process. In Part II we show how the Poisson Equation can form the basis for "Poisson Learning," a new graph-based semi-supervised machine learning algorithm which excels at low label rates.
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University of Minnesota Ph.D. dissertation. 2022. Major: Mathematics. Advisor: Jeff Calder. 1 computer file (PDF); 105 pages.
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Cook, Brendan. (2022). Two Applications of PDEs in Data Science. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/241372.
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