.png){kind=icon}
返回
Broad learning system based on maximum multi-kernel correntropy criterion
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neunet.2024.106521.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The broad learning system (BLS) is an effective machine learning model that exhibits excellent feature extraction ability and fast training speed. However, the traditional BLS is derived from the minimum mean square error (MMSE) criterion, which is highly sensitive to non-Gaussian noise. In order to enhance the robustness of BLS, this paper reconstructs the objective function of BLS based on the maximum multi-kernel correntropy criterion (MMKCC), and obtains a new robust variant of BLS (MKC-BLS). For the multitude of parameters involved in MMKCC, an effective parameter optimization method is presented. The fixed-point iteration method is employed to further optimize the model, and a reliable convergence proof is provided. In comparison to the existing robust variants of BLS, MKC-BLS exhibits superior performance in the non-Gaussian noise environment, particularly in the multi-modal noise environment. Experiments on multiple public datasets and real application validate the efficacy of the proposed method.
Keyword:
Broad learning system
Correntropy
Multi-kernel correntropy
Regression tasks
Predictor
期刊
IF:
6.3
论文数:
8.2K
被引数:
3.0W
机构
引用论文
Multi-kernel correntropy based extended Kalman filtering for state-of-charge estimation基于多核熵的扩展卡尔曼滤波的荷电状态估计.png)
ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏
Facial beauty prediction fusing transfer learning and broad learning system融合迁移学习和广义学习系统的面部美容预测SOFT COMPUTING
OAAI
SOFT COMPUTINGIF2.5
OAAI分享收藏分享收藏
查看更多内容