  Popular domain adaptation (DA) techniques learn a classifier for the target
domain by sampling relevant data points from the source and combining it with
the target data. We present a Support Vector Machine (SVM) based supervised DA
technique, where the similarity between source and target domains is modeled as
the similarity between their SVM decision boundaries. We couple the source and
target SVMs and reduce the model to a standard single SVM. We test the
Coupled-SVM on multiple datasets and compare our results with other popular SVM
based DA approaches.
