In distributed machine learning scenarios, the difference in data distribution among different nodes is a key issue that cannot be ignored. However, existing methods make it difficult to autonomously adjust model parameters for dynamically changing data distributions, leading to inflexible global decision boundaries with insufficient local adaptation. To address this problem, we propose a distributed hybrid support vector machine (SVM) based on the adaptive ensemble selection of regularization parameters, DHS-AE. The model utilizes the data structure information to cut the data space and thus identify data distribution characteristics. The SVM, integrated with regularization parameters that are adaptively determined within specific ranges, is utilized in the local subspace to enable real-time adjustment of decision boundaries in response to distribution changes, thereby further reducing the computational overhead. The generalization bound of DHS-AE is theoretically established using covering numbers, and the fast convergence speed and consistency are derived. In practical applications, we verify the excellent performance of the DHS-AE using a large number of real datasets.
Gong et al. (Thu,) studied this question.