Classifier 1 Domain adaptation with logistic regression incorporating target unlabeled data
1:	Remove domain specific features from the source dataset, using Equation (7).
2:	Initialize TUs = TU and TUh = ø, where TUs is the set of target unlabeled instances with soft labels assigned, TUh is the set of target unlabeled instances with hard labels assigned, and TU is the set of target unlabeled instances passed to the algorithm.
3:	Train a classifier using Equation (8).
4:	Assign labels to the unlabeled instances from the target domain using this classifier. The labels assigned are either: soft and hard labels, hard labels only, or soft labels only. Any instances assigned hard labels are removed from TUs and added to TUh.
5:	while labels assigned to instances in TUs change do
6:	M-step: Train a classifier using Equation (9)., i.e., also use the instances from the target unlabeled dataset that were labeled in steps 3 and 6.
7:	E-step: Same as step 3.
8:	end while
9:	Use classifier trained using Equation (9), on new target instances.