Graphical Multi-Task Learning

Abstract

We investigate the problem of learning multiple tasks that are related according to a network structure, using the multi-task kernel framework proposed by Evgeniou, Micchelli and Pontil. Our method combines a graphical task kernel with an arbitrary base kernel.We demonstrate its effectiveness on a real ecological application that inspired this work.