| DC Field | Value | Language |
|---|
| dc.contributor.author | Zhao Di | - |
| dc.date.accessioned | 2022-05-14T09:32:42Z | - |
| dc.date.available | 2022-05-14T09:32:42Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.citation | Zhao Di. Multi-class classification SVM methods / Zhao Di // Технологии передачи и обработки информации : материалы международного научно-технического семинара, Минск, март-апрель 2022 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2022. – С. 85–87. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/46942 | - |
| dc.description.abstract | Support Vector Machine (SVM) was first proposed by Cortes and Vapnik in 1995. It is developed from the linear separable classification problem. The basic principle of SVM is to use a hyperplane to divide data into two categories, but in many classification problems, the sample is not linear, and the classification problem is multi-classification. We need to solve the multiclassification problem on a dichotomy basis. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | Support Vector Machine | ru_RU |
| dc.subject | multi-classification | ru_RU |
| dc.title | Multi-class classification SVM methods | ru_RU |
| dc.type | Статья | ru_RU |
| Appears in Collections: | Технологии передачи и обработки информации : материалы международного научно-технического семинара (2022)
|
