48945

Автор(ов):

3

Параметры публикации

Тип публикации:

Статья в журнале/сборнике

Название:

Introduction to the theory of randomized machine learning

1860-949Х

DOI:

10.1007/978-3-319-75181-8_10

Наименование источника:

• Studies in Computational Intelligence

Vol. 756

• Berlin

• Springer

2018

Страницы:

199-220
Аннотация
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. Introduction to the theory of randomized machine learning // Studies in Computational Intelligence. 2018. Vol. 756. С. 199-220.