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Solvable Model for the Linear Separability of Structured Data

Linear separability, a core concept in supervised machine learning, refers to whether the labels of a data set can be captured by the simplest possible machine: a linear classifier. In order to quantify linear separability beyond this single bit of information, one needs models of data structure par...

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Detalles Bibliográficos
Autor principal: Gherardi, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999416/
https://www.ncbi.nlm.nih.gov/pubmed/33806454
http://dx.doi.org/10.3390/e23030305