Non-linear Transfer Learning Model
Yuehua Ding · 2009
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.There is a comparison between them in the experiment section.The HiRBF algorithm is also compared with two transfer-unaware approaches.The experiments demonstrate that HiRBF significantly outperforms the others.