首页|Report Summarizes Machine Learning Study Findings from Carnegie Mellon Universit y (Functional Optimal Transport: Regularized Map Estimation and Domain Adaptatio n for Functional Data)
Report Summarizes Machine Learning Study Findings from Carnegie Mellon Universit y (Functional Optimal Transport: Regularized Map Estimation and Domain Adaptatio n for Functional Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news originating fromPittsburgh, Pennsylvania, by NewsR x correspondents, research stated, “We introduce a formulation ofregularized op timal transport problem for distributions on function spaces, where the stochast ic mapbetween functional domains can be approximated in terms of an (infinite-d imensional) Hilbert-Schmidtoperator mapping a Hilbert space of functions to ano ther. For numerous machine learning applications,data can be naturally viewed a s samples drawn from spaces of functions, such as curves and surfaces, inhigh d imensions.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningCa rnegie Mellon University
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