关键词: 正则极限学习机, 多任务, 交替乘子法, 过拟合

Abstract: MT-ELM enables multi-task learning using hidden layers to share data characteristics among different tasks. However, MT-ELM ignores correlation differences between tasks and over-fitting problem. Therefore, MT-RELM is proposed. Firstly, RELM is used to solve the over-fitting problem. Secondly, considering correlation differences between tasks, the constraints are added to output weights based on the assumptions that similar task having similar weight, so this paper uses this constraint to indicate relevance level between tasks. Finally, ADMM algorithm are used to solve MT-RELM model parameters. The results based on the synthetic dataset and wet ball mill dataset show that this algorithm can effectively improve the prediction accuracy and generalization ability.

Key words: regularized extreme learning machine, multi-task, alternating direction method of multipliers, over-fitting