Accession Number : ADA303253
Title : Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge,
Corporate Author : CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE
Personal Author(s) : Thrun, Sebastian ; O'Sullivan, Joseph
PDF Url : ADA303253
Report Date : NOV 1995
Pagination or Media Count : 35
Abstract : 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.
Descriptors : *LEARNING MACHINES, *LEARNING, ALGORITHMS, ROBOTS, CLUSTERING, MOBILE, KNOWLEDGE BASED SYSTEMS.
Subject Categories : Psychology
Distribution Statement : APPROVED FOR PUBLIC RELEASE