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Olivier Grisel,Andreas Mueller,Fabian Pedregosa,Alexandre Gramfort,Gilles Louppe,Peter Prettenhofer,Mathieu Blondel,Vlad Niculae,Arnaud Joly,Joel Nothman,Jake Vanderplas,manoj kumar,Robert Layton,Nelle Varoquaux,Noel Dawe,Johannes Schönberger,Denis A. Engemann,Wei Li,Rajagopalan Raghav,Clay Woolam,Kemal Eren,Eustache,Alexander Fabisch,Alexandre Passos,bthirion,Virgile Fritsch,Danny Sullivan,Hamzeh Alsalhi,Maheshakya Wijewardena +28 more
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The article was published on 2016-04-17 and is currently open access. It has received 1 citations till now.read more
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Scalable, Flexible and Active Learning on Distributions
TL;DR: This thesis investigates approximate embeddings into Euclideanspaces such that inner products in the embedding space approximate kernel values between the source distributions, and provides a greater understanding of the standard tool for doing so on Euclidean inputs, random Fourier features.
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Scalable, Flexible and Active Learning on Distributions
TL;DR: This thesis investigates approximate embeddings into Euclideanspaces such that inner products in the embedding space approximate kernel values between the source distributions, and provides a greater understanding of the standard tool for doing so on Euclidean inputs, random Fourier features.