Machine Learning: Difference between revisions
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(Created page with "=Optimal Transport: Machine Learning= Optimal transport concepts applied to machine learning applications can also be referred to as computational Optimal Transport (OT). At...") |
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=Optimal Transport: Machine Learning= | =Optimal Transport: Machine Learning= | ||
==Introduction== | |||
Optimal transport concepts applied to machine learning applications can also be referred to as computational Optimal Transport (OT). At its core, machine learning focuses on making comparisons between complex objects. To properly measure these similarities, a metric is needed, which is a distance function. | Optimal transport concepts applied to machine learning applications can also be referred to as computational Optimal Transport (OT). At its core, machine learning focuses on making comparisons between complex objects. To properly measure these similarities, a metric is needed, which is a distance function. | ||
Optimal transport respects the underlying structure and geometry of a problem while providing a framework for comparing probability distributions. Optimal transport methods have received attention from researchers in fields as varied as economics, statistics, and quantum mechanics. The categories that OT methods can be divided into include learning, domain adaptation, Bayesian inference, and hypothesis testing. | Optimal transport respects the underlying structure and geometry of a problem while providing a framework for comparing probability distributions. Optimal transport methods have received attention from researchers in fields as varied as economics, statistics, and quantum mechanics. The categories that OT methods can be divided into include learning, domain adaptation, Bayesian inference, and hypothesis testing. | ||
Revision as of 18:43, 9 June 2020
Optimal Transport: Machine Learning
Introduction
Optimal transport concepts applied to machine learning applications can also be referred to as computational Optimal Transport (OT). At its core, machine learning focuses on making comparisons between complex objects. To properly measure these similarities, a metric is needed, which is a distance function.
Optimal transport respects the underlying structure and geometry of a problem while providing a framework for comparing probability distributions. Optimal transport methods have received attention from researchers in fields as varied as economics, statistics, and quantum mechanics. The categories that OT methods can be divided into include learning, domain adaptation, Bayesian inference, and hypothesis testing.