A novel boosting algorithm for multi-task learning based on the Itakuda-Saito divergence

In this paper, we propose a novel multi-task learning algorithm based on an ensemble learning method. We consider a specific setting of the multi-task learning for binary classification problems, in which features are shared among all tasks and all tasks are targets of performance improvement. We focus on a situation that the shared structures among dataset are represented by divergence between underlying distributions associated with multiple tasks. We discuss properties of the proposed method and investigate validity of the proposed method with numerical experiments.