An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine for Classification Problems

An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine for Classification Problems

Sparse Bayesian Extreme Learning Machine (SBELM) constructs an extremely sparse and probabilistic model with low computational cost and high generalization. However, the update rule of hyperparameters (ARD prior) in SBELM involves using the diagonal elements from the inversion of the covariance matr...

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Bibliographic Details
Main Authors: Jiahua Luo, Chi-Man Vong, Zhenbao Liu, Chuangquan Chen
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Inverse-free
quasi-Newton method
sparse Bayesian extreme learning machine
large classification
sparse model
Online Access:https://ieeexplore.ieee.org/document/9458951/

Internet

https://ieeexplore.ieee.org/document/9458951/

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