Transfer Learning by Mapping with Minimal Target Data

Contents

This paper introduces the single-entity-centered setting for transfer across two relational domains. In this setting, target domain data contains information about only a single entity. We present the SR2LR algorithm that finds an effective mapping of the source model to the target domain in this setting and demonstrate its effectiveness in three relational domains. Our experiments additionally show that the most accurate model for the source domain is not always the best model to use for transfer.