Шукати за:
Методологія вибору алгоритму напівкерованого навчання для задач класифікації
Повний текст (PDF)
УДК: 004.855.5(045)
Мова публікації: Англійська
Stuc. intelekt. 2022; 27; (2):46-50
Анотація: The paper concerns the problem of selecting an appropriate semi-supervised learning algorithm based on validating assumptions that the algorithm is based on for the particular dataset. This enables the selection of the fittest algorithm to achieve the best possible model accuracy. In this paper, we provide an overview of four primary semi-supervised assumptions, introduce the definition of metrics used to evaluate the assumption, provide a taxonomy of common semi-supervised algorithms and assumptions based on, and evaluate the proposed methodology on the synthetic too-moons dataset. The results indicate prospects of improving methodologies further by refining and introducing new metrics.
Ключові слова: semi-supervised learning, semi-supervised learning assumptions
Посилання:
- Y.-F. Li and Z.-H. Zhou, "Towards Making Unlabeled Data Never Hurt," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 1, pp. 175-188, Jan. 2015, doi: 10.1109/TPAMI.2014.2299812.
- Semi-Supervised Learning, O., Chapelle, B., Schölkopf, and A., Zien, Eds. (London, U. K.: MIT Press, 2006, pp. 508, ISBN: 978-0-262-03358-9).
- Engelen, J. E., Hoos, H. H. A survey on semi-supervised learning. Mach Learn 109, 373–440 (2020). https://doi.org/10.1007/s10994-019-05855-6.