Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge
Sebastian Thrun and Joseph O'Sullivan
Recently, there has been an increased interest in machine learning methods that learn from more than one learning task. Such methods have repeatedly found to outperform conventional, single-task learning algorithms when learning tasks are appropriately related. To increase robustness of these approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading.
This paper describes the task-clustering (TC) algorithm. TC clusters learning tasks into classes of mutually related tasks. When facing a new thing to learn, TC first determines the most related task cluster, then exploits information selectively from this task cluster only. An empirical study carried out in a mobile robot domain shows that TC outperforms its unselective counterpart in situations where only a small number of tasks is relevant.