Linear and Fisher Separability of Random Points in the d-dimensional Spherical Layer

1 Feb 2020 Sergey Sidorov Nikolai Zolotykh

Stochastic separation theorems play important role in high-dimensional data analysis and machine learning. It turns out that in high dimension any point of a random set of points can be separated from other points by a hyperplane with high probability even if the number of points is exponential in terms of dimension... (read more)

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