Distillation of Knowledge in Boosting Models Научная публикация
| Журнал |
Pattern Recognition and Image Analysis
ISSN: 1054-6618 , E-ISSN: 1555-6212 |
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| Вых. Данные | Год: 2025, Том: 35, Номер: 3, Страницы: 313-318 Страниц : 6 DOI: 10.1134/S1054661825700221 | ||
| Ключевые слова | knowledge distillation, machine learning boosting, overfitting | ||
| Авторы |
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| Организации |
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Информация о финансировании (1)
| 1 | Институт математики им. С.Л. Соболева СО РАН | FWNF-2022-0015 |
Реферат:
The paper explores the possibility of applying the idea of knowledge distillation to the boosting method. The rationale for this approach is that, in many cases, the best forecast quality is achieved in ensembles using trees of excess depth. In these cases, it may be worthwhile to train an ensemble of shallower trees using a deeper model as a "teacher." This makes it possible, in particular, to assess the real "depth" of dependences between variables in a problem, as well as to obtain more visual visualizations of solutions. The study also provides material for understanding the mechanisms of the effectiveness of the knowledge distillation procedure.
Библиографическая ссылка:
Nedel'ko V.M.
Distillation of Knowledge in Boosting Models
Pattern Recognition and Image Analysis. 2025. V.35. N3. P.313-318. DOI: 10.1134/S1054661825700221 WOS Scopus РИНЦ
Distillation of Knowledge in Boosting Models
Pattern Recognition and Image Analysis. 2025. V.35. N3. P.313-318. DOI: 10.1134/S1054661825700221 WOS Scopus РИНЦ
Даты:
| Поступила в редакцию: | 25 мар. 2025 г. |
| Принята к публикации: | 9 апр. 2025 г. |
| Опубликована в печати: | 23 окт. 2025 г. |
| Опубликована online: | 23 окт. 2025 г. |
Идентификаторы БД:
| Web of science: | WOS:001597069500018 |
| Scopus: | 2-s2.0-105019389334 |
| РИНЦ: | 83051313 |
Цитирование в БД:
Пока нет цитирований