Sparse Twin Extreme Learning Machine With <inline-formula> <tex-math notation="LaTeX">$\varepsilon$ </tex-math></inline-formula>-Insensitive Zone Pinball Loss

Sparse Twin Extreme Learning Machine With <inline-formula> <tex-math notation="LaTeX">$\varepsilon$ </tex-math></inline-formula>-Insensitive Zone Pinball Loss

Twin extreme learning machine (TELM) based on the hinge-loss function shows great potential for pattern classification. However, the hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for resampling. In contrast, the...

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Bibliographic Details
Main Authors: Jumei Shen, Jun Ma
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Twin extreme learning machine
pinball loss
hinge loss
sparsity
noise insensitivity
Online Access:https://ieeexplore.ieee.org/document/8795493/

Internet

https://ieeexplore.ieee.org/document/8795493/

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