08/06/2021: TS. Nguyễn Vũ Linh (Eindhoven Univ. Tech., Netherlands), Examples of Classification Tasks

Classification is a machine learning approach in which the computer learns a map from objects (and their features) to classes. Numerous studies have focused on the setting of multi-class classification with a single class variable. More general classification tasks are to predict multiple class variables simultaneously, i.e. multi-dimensional classification (MDC). When the class variables are binary, the MDC task is reduced to a multi- labelclassification(MLC) task. In this talk, we shall quickly illustrate those classification tasks (using examples). The talk also mentions ensemble learning and active learning which are respectively used to seek better predictive performance on a predictive modeling task than a single predictive model and to exploit labeled data in the most effective way. Making set-valued predictions in classification tasks will be discussed. Incases of uncertainty, a predictive model preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. When making set-valued predictions, the classifier should strive for an optimal balance between the correctness (the true class is among the candidates) and the precision(the candidates are not too many) of its prediction.

Speaker: TS. Nguyễn Vũ Linh, Eindhoven Univ. Tech.

Time: 15:30, Tuesday, June 08, 2021

Venue: Webinar; Access code: https://bit.ly/3uYyqpz