.png){kind=icon}
.png){kind=icon}
Return
Weighted Generalized Cross-Validation-Based Regularization for Broad Learning System
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2020.3015749.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The broad learning system (BLS) is an emerging flat network, which has demonstrated its outstanding performance in classification and regression problems. The regularization plays an important role in the performance of the BLS. In real applications, since the BLS network is usually expanded dynamically, a predetermined regularization parameter may reduce the performance of the network. Using a fixed regularization in some cases, the classification accuracy of the BLS decreases dramatically when we expand the network. To alleviate this problem, we propose a method that automatically finds appropriate regularization parameters for different datasets, which is based on the weighted generalized cross-validation (WGCV). The experimental results indicate that the WGCV method improves the performance of the BLS, and alleviates the accuracy decrease of the incremental learning algorithm.
Keywords:
Learning systems
Neural networks
Computer science
Feature extraction
Zinc
Cybernetics
Gallium nitride
Broad learning system (BLS)
classification
incremental learning
weighted generalized cross-validation (WGCV)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
View more organizations