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Regularization of Machine Learning and Linear Algebra. / Liu, Shuang; Kabanikhin, Sergey Igorevich; Strijhak, Sergei Vladimirovich.

CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence. ed. / Chen Xiangqun; Fang Juan. Association for Computing Machinery, 2025. p. 327-332.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Liu, S, Kabanikhin, SI & Strijhak, SV 2025, Regularization of Machine Learning and Linear Algebra. in C Xiangqun & F Juan (eds), CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence. Association for Computing Machinery, pp. 327-332, 8th International Conference on Computer Science and Artificial Intelligence, Beijing, China, 06.12.2024. https://doi.org/10.1145/3709026.3709071

APA

Liu, S., Kabanikhin, S. I., & Strijhak, S. V. (2025). Regularization of Machine Learning and Linear Algebra. In C. Xiangqun, & F. Juan (Eds.), CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence (pp. 327-332). Association for Computing Machinery. https://doi.org/10.1145/3709026.3709071

Vancouver

Liu S, Kabanikhin SI, Strijhak SV. Regularization of Machine Learning and Linear Algebra. In Xiangqun C, Juan F, editors, CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence. Association for Computing Machinery. 2025. p. 327-332 doi: 10.1145/3709026.3709071

Author

Liu, Shuang ; Kabanikhin, Sergey Igorevich ; Strijhak, Sergei Vladimirovich. / Regularization of Machine Learning and Linear Algebra. CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence. editor / Chen Xiangqun ; Fang Juan. Association for Computing Machinery, 2025. pp. 327-332

BibTeX

@inproceedings{da22e75ebaf245f2a64e354c1e65caef,
title = "Regularization of Machine Learning and Linear Algebra",
abstract = "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.",
keywords = "linear neural network, machine learning, system of linear algebraic equations",
author = "Shuang Liu and Kabanikhin, {Sergey Igorevich} and Strijhak, {Sergei Vladimirovich}",
note = "The authors thank the referees for their careful review of the paper and their comments. This work was supported by the program for fundamental scientific research of the Siberian Branch of the Russian Academy of Sciences (No. 2024-0001), the Russian Foundation for Basic Research (No. 03-01-11111), and the China Scholarship Council (No. 202008230141). And the work was supported by a grant for research centers in the field of artificial intelligence (No. 000000D730321P5Q0002).; 8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024 ; Conference date: 06-12-2024 Through 08-12-2024",
year = "2025",
month = feb,
day = "15",
doi = "10.1145/3709026.3709071",
language = "English",
isbn = "9798400718182",
pages = "327--332",
editor = "Chen Xiangqun and Fang Juan",
booktitle = "CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence",
publisher = "Association for Computing Machinery",
address = "United States",

}

RIS

TY - GEN

T1 - Regularization of Machine Learning and Linear Algebra

AU - Liu, Shuang

AU - Kabanikhin, Sergey Igorevich

AU - Strijhak, Sergei Vladimirovich

N1 - Conference code: 8

PY - 2025/2/15

Y1 - 2025/2/15

N2 - 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.

AB - 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.

KW - linear neural network

KW - machine learning

KW - system of linear algebraic equations

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85219594269&origin=inward&txGid=4fbd80970b131287e75d4dfd57829bfa

UR - https://www.mendeley.com/catalogue/bef1d79a-e7c9-3b7e-a84d-a666173c46a0/

U2 - 10.1145/3709026.3709071

DO - 10.1145/3709026.3709071

M3 - Conference contribution

SN - 9798400718182

SP - 327

EP - 332

BT - CSAI 2024 - Proceedings of 2024 8th International Conference on Computer Science and Artificial Intelligence

A2 - Xiangqun, Chen

A2 - Juan, Fang

PB - Association for Computing Machinery

T2 - 8th International Conference on Computer Science and Artificial Intelligence

Y2 - 6 December 2024 through 8 December 2024

ER -

ID: 64991087