Transfer Learning by Mapping with Minimal Target Data
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 demonstsrate 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.