| Issue |
ITM Web Conf.
Volume 72, 2025
III International Workshop on “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-III 2024)
|
|
|---|---|---|
| Article Number | 04010 | |
| Number of page(s) | 7 | |
| Section | Data Mining, Machine Learning and Pattern Recognition | |
| DOI | https://doi.org/10.1051/itmconf/20257204010 | |
| Published online | 13 February 2025 | |
Comparison of cluster and classical approaches to issuing training information in adaptive learning systems
1 Siberian Federal University, Krasnoyarsk, Russia
2 Krasnoyarsk State Agrarian University, Krasnoyarsk, Russia
3 Navoi State University of Mining and Technologies, Navoi, Uzbekistan
* Corresponding author: kostyan785@mail.ru
A comparison of cluster and classical approaches to issuing training information in adaptive training systems is performed. The adaptive training method of L.A. Rastrigin based on the frequency multilingual dictionary on system analysis and information technology is taken as a basis. The quality criterion of training of this method is configured to interact with the correlation matrix of terms obtained during the frequency analysis of the collection of text corpora. The resulting cluster approach shows better results compared to the classical one in the context of issuing terms that are most relevant to the lexical goals of the learner.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.