КФУ. Карточка публикации. Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

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REDUCTION OF THE DIMENSION OF NEURAL NETWORK MODELS IN PROBLEMS OF PATTERN RECOGNITION AND FORECASTING

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2017

Язык

английский

Авторы, сотрудники КФУ

Бочкарев Владимир Владимирович, автор

Библиографическое описание на языке оригинала

Nasertdinova A.D, Bochkarev V.V., Reduction of the dimension of neural network models in problems of pattern recognition and forecasting//Journal of Physics: Conference Series. - 2017. - Vol.929, Is.1. - Art. № 012038.

Аннотация

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good.

Ключевые слова

neural networks, principal component analysis, reduction of the dimension, MNIST

Название журнала

Journal of Physics: Conference Series

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039072328&doi=10.1088%2f1742-6596%2f929%2f1%2f012038&partnerID=40&md5=c91b8b82d293cd298f12f06a5878020a

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https://repository.kpfu.ru/?p_id=174165

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Бочкарев Владимир Владимирович	ru_RU
dc.date.accessioned	2017-01-01T00:00:00Z	ru_RU
dc.date.available	2017-01-01T00:00:00Z	ru_RU
dc.date.issued	2017	ru_RU
dc.identifier.citation	Nasertdinova A.D, Bochkarev V.V., Reduction of the dimension of neural network models in problems of pattern recognition and forecasting//Journal of Physics: Conference Series. - 2017. - Vol.929, Is.1. - Art. № 012038.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=174165	ru_RU
dc.description.abstract	Journal of Physics: Conference Series	ru_RU
dc.description.abstract	Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	neural networks	ru_RU
dc.subject	principal component analysis	ru_RU
dc.subject	reduction of the dimension	ru_RU
dc.subject	MNIST	ru_RU
dc.title	Reduction of the dimension of neural network models in problems of pattern recognition and forecasting	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU