Gaining additional labels for data: An introduction to using semisupervised learning for real problems

There are sometimes occasions where the labels on data are insufficient. In such situations, semisupervised learning can be of great practical value. Yingsong Zhang explores illustrative examples of how to come up with creative solutions, derived from textbook approaches.

Training a model requires some information to guide it to a useful conclusion. The information is often in the form of human-domain knowledge, which mostly appears as labels. However, labels are not always available or in the format that we would wish for. Yingsong Zhang walks you through three situations to illustrate how to apply semisupervised learning to real problems: