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Linear and Fisher Separability of Random Points in the d-Dimensional Spherical Layer and Inside the d-Dimensional Cube
Stochastic separation theorems play important roles in high-dimensional data analysis and machine learning. It turns out that in high dimensional space, 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 expon...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712262/ https://www.ncbi.nlm.nih.gov/pubmed/33287049 http://dx.doi.org/10.3390/e22111281 |