Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering 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: 1, | ||||
Ключевые слова | Weakly supervised regression, Manifold regularization, Co-association matrix, Fuzzy clustering, Cluster ensemble | ||||
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Организации |
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Информация о финансировании (1)
1 | Российский научный фонд | 24-21-00195 |
Реферат:
Weakly supervised learning algorithms have become increasingly important for modeling complex systems where precise labels are scarce or expensive to obtain. There are specialized algorithms for solving the weakly supervised regression problem, such as the Weakly Supervised Regression algorithm [1], which is based on manifold regularization and cluster ensemble. In this article, we introduce novel improvements to original algorithm, that significantly increase the stability and quality of the algorithm and reduce its dependence on properly selected hyperparameters. This result is achieved through the use of fuzzy clustering and consistency weights when constructing a cluster ensemble.
Библиографическая ссылка:
Kalmutskiy K.
, Berikov V.
Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering
Pattern Recognition and Image Analysis. 2025. V.35. N1.
Enhancing Stability of the Weakly Supervised Regression Algorithm Using Manifold Regularization and Fuzzy Clustering
Pattern Recognition and Image Analysis. 2025. V.35. N1.
Даты:
Поступила в редакцию: | 18 нояб. 2024 г. |
Принята к публикации: | 13 дек. 2024 г. |
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