Skip to yearly menu bar Skip to main content


Learning Bound for Parameter Transfer Learning

Wataru Kumagai

Area 5+6+7+8 #168

Keywords: [ Regularization and Large Margin Methods ] [ Sparsity and Feature Selection ] [ Learning Theory ]


We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability of parametric feature mapping and parameter transfer learnability, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in self-taught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning.

Live content is unavailable. Log in and register to view live content