Multi-Task Learning Based on Stochastic Configuration Networks

When the human brain learns multiple related or continuous tasks, it will produce knowledge sharing and transfer. Thus, fast and effective task learning can be realized. This idea leads to multi-task learning. The key of multi-task learning is to find the correlation between tasks and establish a fast and effective model based on these relationship information. This paper proposes a multi-task learning framework based on stochastic configuration networks. It organically combines the idea of the classical parameter sharing multi-task learning with that of constraint sharing configuration in stochastic configuration networks. Moreover, it provides an efficient multi-kernel function selection mechanism. The convergence of the proposed algorithm is proved theoretically. The experiment results on one simulation data set and four real life data sets verify the effectiveness of the proposed algorithm.