39510

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

On one extremal problem of adaptive machine learning for detection of anomalies

ISBN/ISSN: 

0005-1179

DOI: 

10.1134/S0005117908060052

Наименование источника: 

  • Automation and Remote Control

Обозначение и номер тома: 

V. 69, No. 6

Город: 

  • Москва

Издательство: 

  • PLEIADES PUBLISHING,Ltd

Год издания: 

2008

Страницы: 

942–952
Аннотация
An adaptive algorithm to solve a wide range of problems of unsupervised learning by constructing a sequence of interrelated extremal principles was proposed. The least squares method with a priori defined weights used as a starting point enabled determination of the “center” of learning sample. Next, a natural passage from the least squares method to more flexible extremal principle enabling adaptive determination of both the “center” and weights of the learning sample events was performed. Finally, a universal extremal principle enabling determination of the scaling coefficient of the membership function in addition to the “center” and weights was constructed.

Библиографическая ссылка: 

Туницкий Д.В., Мальков К.В. On one extremal problem of adaptive machine learning for detection of anomalies // Automation and Remote Control. 2008. V. 69, No. 6. С. 942–952.

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