Prof. dr. M. Loog, Technische Universiteit Delft
Linear, Discriminative, Semi-Supervised Classifiers
Learning methods are at the heart of almost any modern computer application. Supervised learning algorithms [e.g. classifiers and decision rules] are able to generalize from examples and predict the desired output to unseen input. A major obstacle in their successful use is the need for sufficient expert-labeled examples to learn from. Semi-supervised learning [SSL] promises to improve radically upon this situation by exploiting both labeled and unlabeled data. To this date, however, SSL has not lived up to this promise, often even deteriorating instead of improving performance. Current methods can be difficult to handle, especially by the non-expert, and they are not as widely used as their supervised counterparts. We therefore need SSL methods that are reliable and can be readily substituted for the supervised classifiers that are en vogue in the different research domains and application areas.