@inproceedings{6a887d4eff234e8ab996a9045ff24067,
title = "Fusion-based multi-kernel learning filter with maximum correntropy criterion",
abstract = "Kernel learning filters have been effective tools for addressing nonlinear function fitting problem. In these filters with Gaussian kernels, the performance depends on the choice of the kernel width and an inappropriate kernel width might degrade the learning performance. To further improve the learning performance, a fusion-based multi-kernel learning filter with maximum correntropy criterion is proposed in this paper, in which multiple learning filters with different kernel widths run independently and the output estimates are fused by a set of weighting coefficients. The weighting coefficients are treated as the posterior probabilities of the kernel widths in effective and they are computed recursively by using the likelihood functions. Simulation results show that the proposed filter outperforms the existing single kernel learning filters and the multi-kernel learning filter with maximum mixture correntropy criterion.",
keywords = "Kernel learning, Maximum correntropy",
author = "Lin Chu and Wenling Li",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020 ; Conference date: 20-11-2020 Through 22-11-2020",
year = "2020",
month = nov,
day = "20",
doi = "10.1109/DDCLS49620.2020.9275051",
language = "英语",
series = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "556--561",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2020 IEEE 9th Data Driven Control and Learning Systems Conference, DDCLS 2020",
address = "美国",
}