Solving weakly supervised multi-output regression Тезисы доклада
| Конференция |
Международная конференция КОМПЬЮТЕРНЫЕ ДАННЫЕ
АНАЛИЗ И МОДЕЛИРОВАНИЕ: СТОХАСТИКА И
НАУКА ДАННЫХ 06-10 сент. 2022 , Минск |
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| Сборник | Computer data analysis and modeling: Stochastics and data science : Proceedings of the XIII International Conference. Minsk, 2022 Сборник, Belarusian State University. Minsk.2022. 250 c. ISBN 978-985-881-420-5. |
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| Вых. Данные | Год: 2022, Страницы: 83-86 Страниц : 4 | ||||
| Ключевые слова | data science, supervised regression, wasserstein distance | ||||
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Информация о финансировании (1)
| 1 | Российский научный фонд | 22-21-00261 |
Реферат:
We propose a solution to the multi-output weakly supervised regression problem. In the studied setting the observed data is partly labeled, and known labels are considered to be the probability distribution to represent possible uncertainty in labeling due to noise. The proposed solution consists in minimizing the Wasser-stein distance between multivariate normal distributions, and approximation of matrices having a low-rank format. In the experimental part of the paper we provide the results, which are shown to be superior to the previous methods on Monte-Carlo simulations and a real dataset.
Библиографическая ссылка:
Kondratyev V.
, Berikov V.
Solving weakly supervised multi-output regression
В сборнике Computer data analysis and modeling: Stochastics and data science : Proceedings of the XIII International Conference. Minsk, 2022. – Belarusian State University., 2022. – C.83-86. – ISBN 978-985-881-420-5. РИНЦ
Solving weakly supervised multi-output regression
В сборнике Computer data analysis and modeling: Stochastics and data science : Proceedings of the XIII International Conference. Minsk, 2022. – Belarusian State University., 2022. – C.83-86. – ISBN 978-985-881-420-5. РИНЦ
Идентификаторы БД:
| РИНЦ: | 65639644 |
Цитирование в БД:
Пока нет цитирований