Year: 2024
Type: article
Abstract: In recent years, Sigma-Pi-Sigma neural network (SPSNN) as a special kind of higher-order neural network has attracted wide attention for its fast convergence speed and good approximation ability. However, an inappropriate number of hidden layer neurons may also lead to model underfitting or overfitting, which affects the performance and generalization ability of the model. Therefore, we propose a Sigma-Pi-Sigma neural network with graph regularity by adding a graph regularity term to the network. The results show that the proposed algorithm performs well in terms of training accuracy, testing accuracy and efficiency.
DOI: 10.54097/xwvpkd67
Authors: Qianru Huang, Qingmei Dong, Yunlong Liu, Deqing Ji, Qinwei Fan
Institution: Xi'an Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4393054777