Conference Proceedings

Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge

X Wu, JH Manton, U Aickelin, J Zhu

IEEE International Symposium on Information Theory: Proceedings | IEEE | Published : 2021

Abstract

Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem. On the other hand, it is also recognized that if not executed properly, transfer learning algorithms could in fact impair the learning performance instead of improving it - commonly known as negative transfer. In this paper, we study the online transfer learning problems where the source samples are given in an off-line way while the target samples arrive sequentially. We define the expected regret of the online transfer learning problem, and provi..

View full abstract