Robust Fisher-Regularized Twin Extreme Learning Machine with Capped <i>L</i><sub>1</sub>-Norm for Classification

Robust Fisher-Regularized Twin Extreme Learning Machine with Capped <i>L</i><sub>1</sub>-Norm for Classification

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first come up with a Fisher-regularized twin extreme lea...

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Bibliographic Details
Main Authors:	Zhenxia Xue, Linchao Cai
Format:	Article
Language:	English
Published:	MDPI AG 2023-07-01
Series:	Axioms
Subjects:	twin extreme learning machine within-class scatter fisher regularization capped <i>L</i><sub>1</sub>-norm robustness
Online Access:	https://www.mdpi.com/2075-1680/12/7/717

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