48941

Автор(ы): 

Автор(ов): 

3

Параметры публикации

Тип публикации: 

Доклад

Название: 

Randomized machine learning: Statement, solution, applications

DOI: 

10.1109/IS.2016.7737456

Наименование конференции: 

  • 8th IEEE International Conference on Intelligent Systems (IS 2016)

Наименование источника: 

  • Proceedings of IEEE 8th International Conference on Intelligent Systems (IS-2016)

Город: 

  • Boston, USA

Издательство: 

  • IEEE

Год издания: 

2016

Страницы: 

27-39
Аннотация
In this paper we propose a new machine learning concept called randomized machine learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from “classical” machine learning is that optimal estimation deals with the probability density functions of random parameters and the “worst” probability density of random data errors. As the optimality criterion of estimation, randomized machine learning employs the generalized information entropy maximized on a set described by the system of empirical balances. We apply this approach to text classification and dynamic regression problems. The results illustrate capabilities of the approach.

Библиографическая ссылка: 

Попков Ю.С., Дубнов Ю.А., Попков А.Ю. Randomized machine learning: Statement, solution, applications / Proceedings of IEEE 8th International Conference on Intelligent Systems (IS-2016). Boston, USA: IEEE, 2016. С. 27-39.