Abstract: Multi-task learning utilizes labeled data from other "similar" tasks and can achieve efficient knowledge-sharing between tasks. Previous research mainly focused on multi task learning for linear regression. A novel Bayesian multi-task learning model for non-linear regression, i. e. HiRBF, was proposed. HiRBF is constructed under a hierarchical Bayesian framework. According to whether the input to-hidden is shared by all tasks or not, we have two options to build the HiRBF model. Inhere is a comparison between them in the experiment section. The HiRBF algorithm is also compared with two transfer-unaware approaches. The experiments demonstrate that HiRI3F significantly outperforms the others.

Key words: Transfer learning,Bayesian hierarchical model,Regression,RBF network