Semi-supervised Learning - Methods For Semi-supervised Learning - Heuristic Approaches

Heuristic Approaches

Some methods for semi-supervised learning are not intrinsically geared to learning from both unlabeled and labeled data, but instead make use of unlabeled data within a supervised learning framework. For instance, the labeled and unlabeled examples may inform a choice of representation, distance metric, or kernel for the data in an unsupervised first step. Then supervised learning proceeds from only the labeled examples.

Self-training is a wrapper method for semi-supervised learning. First a supervised learning algorithm is used to select a classifier based on the labeled data only. This classifier is then applied to the unlabeled data to generate more labeled examples as input for another supervised learning problem. Generally only the labels the classifier is most confident of are added at each step.

Co-training is an extension of self-training in which multiple classifiers are trained on different (ideally disjoint) sets of features and generate labeled examples for one another.