神经网络训练中的随机搜索

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

PATTERN RECOGNITION AND IMAGE ANALYSIS Pub Date : 2024-07-04 DOI:10.1134/s105466182470010x

V. V. Krasnoproshin, V. V. Matskevich

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引用次数: 0

摘要

摘要本文论述了与神经网络训练有关的最新应用问题。研究表明，随着实际问题范围的扩大，梯度法并不总能满足该主题领域的条件，这有助于开发替代训练方法。本文提出了一种实现退火法的原始训练算法，并证明了该算法对最优解的收敛性。该算法的改进版已被开发出来，它与训练样本的大小无关。实验研究（以解决图像分类和彩色图像压缩问题为例）证实了所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Random Search in Neural Networks Training

Abstract

The paper deals with a state-of-art applied problem related to the neural networks training. It is shown that, given the expansion of the range of practical problems, gradient methods do not always satisfy the conditions of the subject area, which contributes to the development of alternative training methods. An original training algorithm is proposed that implements the annealing method, for which convergence to the optimal solution is proven. A modified version of the algorithm has been developed that is invariant to the size of the training sample. Experimental studies (using the example of solving problems of image classification and color image compression) confirm the effectiveness of the proposed approach.

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来源期刊

PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.80

自引率

20.00%

发文量

期刊介绍： The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.