Abstract
Semi-supervised learning, or SSL, falls somewhere between supervised and
unsupervised learning. The algorithm is provided with some supervision data in
addition to unlabeled data. There are two primary learning paradigms in it.
Transductive education aims to use the trained classifier on unlabeled instances
observed during training. This kind of algorithm is mainly used for node embedding on
graphs, like random walks, where the goal is to label the graph's unlabeled nodes at the
training time. Inductive learning aims to develop a classifier that can generalize
unobserved situations during a test. This chapter details different semi-supervised
algorithms in healthcare.