Use of self-organization principles in construction of hierarchical neural network classifiersстатья
Статья опубликована в журнале из списка RSCI Web of Science
Статья опубликована в журнале из перечня ВАК
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 29 мая 2015 г.
Местоположение издательства:Road Town, United Kingdom
Первая страница:122
Последняя страница:124
Аннотация:The paper describes the basic principles underlying the operation of a new algorithm for the construction of hierarchical neural network classifiers. The algorithm is based on a modification of the error backpropagation method which permits supervised learning in the self-organization mode. With the algorithm applied recursively, it is possible to construct compact and computationally efficient self-organizing structures for neural-network classifiers. A study into the operation of the algorithm on several model and real-world tasks is summed up.