Multi-view Semi-supervised Learning Using Privileged Information

Evgueni Smirnov*, Richard Delava, Ron Diris, Nikolay Nikolaev

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


In this paper we propose to combine the paradigm of multi-view semi-supervised learning with that of learning using privileged information. The combination is realized by a new method that we introduce in detail. A distinctive feature of the method is that it is classifier agnostic which contracts with most of the methods for learning using privileged information. An experimental study on a real-life problem shows that using privileged information is capable of improving multi-view semi-supervised learning.
Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 24th International Conference, EAAAI/EANN 2023, Proceedings
EditorsLazaros Iliadis, Ilias Maglogiannis, Serafin Alonso, Chrisina Jayne, Elias Pimenidis
Number of pages9
Volume1826 CCIS
ISBN (Print)9783031342035
Publication statusPublished - 1 Jan 2023
Event24th International Conference on Engineering Applications of Neural Networks - León, Spain
Duration: 14 Jun 202317 Jun 2023
Conference number: 24

Publication series

SeriesCommunications in Computer and Information Science
Volume1826 CCIS


Conference24th International Conference on Engineering Applications of Neural Networks
Abbreviated titleEANN / EAAAI 2023
Internet address


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