Kernel‐risk‐sensitive conjugate gradient algorithm with Student's‐t distribution based random fourier features

Kernel‐risk‐sensitive loss (KRSL) achieves an efficient performance surface, which has been applied in the kernel adaptive filters (KAFs) successfully. However, the KRSL based KAFs use the stochastic gradient descent (SGD) method in the optimization, which usually suffer from inadequate accuracy wit...

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Bibliographic Details

Published in:	Electronics letters Vol. 59; no. 9
Main Authors:	Tang, Shenjie, Li, Xifeng, Bi, Dongjie, Tang, Yu, Xie, Xuan, Li, Zhenggui, Xie, Yongle
Format:	Journal Article
Language:	English
Published:	Stevenage John Wiley & Sons, Inc 01.05.2023
Subjects:	Accuracy Adaptive filters adaptive signal processing Algorithms Conjugate gradient method Convexity Dictionaries Fourier transforms Methods Normal distribution Optimization Risk
ISSN:	0013-5194, 1350-911X
Online Access:	Get full text
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