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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/3/305 |