Transfer learning and kernel based multitask learning

I have a number of papers on transfer learning that I have started looking through for one of my research projects. Transfer learning is a machine learning technique where the basic idea is to learn multiple related tasks simultaneously, rather than learn each task independently. Previous work have shown that this works better, especially for predictive performance, compared to independent, single task learning. For my problem, I am interested in using multitask learning techniques based on natural extensions of kernel based methods for single task learning, such as the Support Vector Machines (SVMs).