Реферат: This paper explores the connection between systems of linear algebraic equations (SLAE) and machine learning methods, including regularization techniques, to establish a more novel neural network model based on linear neural networks. The goal is to construct a weight matrix for the neural network, which, by simulating the process of finding pseudo-solutions to SLAE, can generate the optimal answer for any input data. In this new neural network model, linear operations are performed first, followed by nonlinear operations, ultimately yielding an optimized weight matrix that serves as the pseudo-solution to the SLAE. The paper demonstrates how linear neural networks can be simplified to SLAE, how adding nonlinear layers to the linear neural network model can improve accuracy, and how machine learning methods can be used to find pseudo-solutions to SLAE.

Библиографическая ссылка: Liu S. , Kabanikhin S.I. , Strijhak S.V.
Regularization of Machine Learning and Linear Algebra
В сборнике Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence. – ACM., 2024. – C.327-332. – ISBN 979-8-4007-1818-2. DOI: 10.1145/3709026.3709071 Scopus OpenAlex

Даты:

Опубликована в печати:	15 февр. 2025 г.
Опубликована online:	15 февр. 2025 г.

Идентификаторы БД:

Scopus:	2-s2.0-85219594269
OpenAlex:	W4407601566

Цитирование в БД: Пока нет цитирований

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