Self-supervised Learning in Symbolic Classification

Xenia Naidenova, Sergey Kurbatov

2021

Abstract

A new approach to modelling self-supervised learning for automated constructing and improving algorithms of inferring logical rules from examples is advanced. As a concrete model, we consider the process of inferring good maximally redundant classification tests or minimal formal concepts. The concepts of external and internal learning contexts are introduced. A model of intelligent agent capable of improving its learning process is considered. It is shown that the same learning algorithm can be used in both external and internal learning contexts.

Paper Citation

in Harvard Style

Naidenova X. and Kurbatov S. (2021). Self-supervised Learning in Symbolic Classification. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 289-294. DOI: 10.5220/0010732700003101

in Bibtex Style

@conference{bml21,
author={Xenia Naidenova and Sergey Kurbatov},
title={Self-supervised Learning in Symbolic Classification},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732700003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Self-supervised Learning in Symbolic Classification
SN - 978-989-758-559-3
AU - Naidenova X.
AU - Kurbatov S.
PY - 2021
SP - 289
EP - 294
DO - 10.5220/0010732700003101