A survey on semi-supervised learning - Springer

← Back to document page

machine learning (2020) 109:373 440 survey on semi-supervised learningJesper E. van Engelen1 Holger H. Hoos1,2Received: 3 December 2018 / Revised: 20 September 2019 / Accepted: 29 September 2019 /Published online: 15 November 2019 The Author(s) 2019AbstractSemi- supervised learning is the branch of machine learning concerned with using labelledas well as unlabelled data to perform certain learning tasks. Conceptually situated betweensupervised and unsupervised learning , it permits harnessing the large amounts of unlabelleddata available in many use cases in combination with typically smaller sets of labelled recent years, research in this area has followed the general trends observed in machinelearning, with much attention directed at neural network-based models and generative learn-ing. The literature on the topic has also expanded in volume and scope, now encompassing abroad spectrum of theory, algorithms and applications.

Semi-supervised learning is a branch of machine learning that aims to combine these two tasks (Chapelle et al. 2006b;Zhu2008). Typically, semi-supervised learning algorithms attempt to improve performance in one of these two tasks by …

  Machine, Learning, Supervised, Machine learning, Supervised learning