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Kernel Mean Embedding of Distributions: A Review and Beyond
2017
Foundations and Trends® in Machine Learning
A Hilbert space embedding of a distribution---in short, a kernel mean embedding---has recently emerged as a powerful tool for machine learning and inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. While
doi:10.1561/2200000060
fatcat:vgmsbodozngltpzy6c2idxnx34