| DC Field | Value | Language |
|---|
| dc.contributor.author | Zhao Di | - |
| dc.contributor.author | Tang Yi | - |
| dc.coverage.spatial | Минск | ru_RU |
| dc.date.accessioned | 2023-06-30T05:56:48Z | - |
| dc.date.available | 2023-06-30T05:56:48Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Zhao Di. The adaptive boosting algorithm in biomedical image segmentation / Zhao Di, Tang Yi // Информационные технологии и управление : материалы 59-ой научной конференции аспирантов, магистрантов и студентов, Минск, 17–21 апреля 2023 года / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: Л. Ю. Шилин [и др.]. – Минск, 2023. – С. 56. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/52377 | - |
| dc.description.abstract | Adaptive Boosting is a powerful machine learning algorithm that has been widely used in biomedical image
segmentation due to its ability to handle high-dimensional feature spaces and improve classication accuracy.
In this paper, we will introduce the principles and applications of AdaBoost and discuss its advantages and
disadvantages. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | machine learning | ru_RU |
| dc.subject | magnetic resonance imaging | ru_RU |
| dc.subject | biomedical image | ru_RU |
| dc.title | The adaptive boosting algorithm in biomedical image segmentation | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | Информационные технологии и управление : материалы 59-й научной конференции аспирантов, магистрантов и студентов (2023)
|
