  Data quantity and quality are crucial factors for data-driven learning
methods. In some target problem domains, there are not many data samples
available, which could significantly hinder the learning process. While data
from similar domains may be leveraged to help through domain adaptation,
obtaining high-quality labeled data for those source domains themselves could
be difficult or costly. To address such challenges on data insufficiency for
classification problem in a target domain, we propose a weak adaptation
learning (WAL) approach that leverages unlabeled data from a similar source
domain, a low-cost weak annotator that produces labels based on task-specific
heuristics, labeling rules, or other methods (albeit with inaccuracy), and a
small amount of labeled data in the target domain. Our approach first conducts
a theoretical analysis on the error bound of the trained classifier with
respect to the data quantity and the performance of the weak annotator, and
then introduces a multi-stage weak adaptation learning method to learn an
accurate classifier by lowering the error bound. Our experiments demonstrate
the effectiveness of our approach in learning an accurate classifier with
limited labeled data in the target domain and unlabeled data in the source
domain.
