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Some Applications of Machine Learnin...

Ologbonyo, Joshua,

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Some Applications of Machine Learning with Tensors /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Some Applications of Machine Learning with Tensors // Joshua Ologbonyo.
Author:	Ologbonyo, Joshua,
Description:	1 electronic resource (185 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31844738
ISBN:	9798315763512

Some Applications of Machine Learning with Tensors /
Ologbonyo, Joshua,

Some Applications of Machine Learning with Tensors /Joshua Ologbonyo. - 1 electronic resource (185 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Complex problems in scientific computing often rely on computationally expensive methodologies, both in terms of execution time and storage requirements. Many real-world applications involve data represented as high-dimensional tensors, which are costly to generate or collect, and present significant challenges for machine learning or numerical methods due to their sheer volume and dimensional complexity. Research in data compression has demonstrated that large datasets can be effectively compressed, significantly reducing computational demands while maintaining reliable levels of accuracy. However, many existing approaches involve flattening tensorial data, which often compromises the intrinsic structure and meaning. This issue, combined with the pervasive curse of dimensionality and the loss of structural information when vectorizing or matricizing data, highlights the need for specialized low-rank tensor machine learning methods that preserve the data's inherent high dimensional structure and meaning.This work applies advanced computational techniques to tackle real-world challenges in data-driven modeling and prediction across diverse domains, including spatial wildfire modeling, wildfire remote-sensing data analysis, biochemical systems, and astrophysical simulations. By leveraging machine learning, tensor-based methods, stochastic modeling and a combination of machine learning and tensor-based methods, the study provides valuable insights into the behaviors of complex systems. These approaches not only enhance our understanding of intricate phenomena and underlying dynamics of complex systems but also enhance the accuracy and efficiency of predictive models, aid in decision-making processes, offering scalable and efficient solutions to problems in wildfire management, biochemical system simulation, and computational astrophysics.

English

ISBN: 9798315763512Subjects--Topical Terms:

515831
Mathematics.
Subjects--Index Terms:

Machine learning

Some Applications of Machine Learning with Tensors /
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500 $a Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
500 $a Advisors: Sidje, Roger B. Committee members: Halpern, David; Rasoulzadeh, Mojdeh; Zhu, Wei; Gleyzer, Sergei.
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520 $a Complex problems in scientific computing often rely on computationally expensive methodologies, both in terms of execution time and storage requirements. Many real-world applications involve data represented as high-dimensional tensors, which are costly to generate or collect, and present significant challenges for machine learning or numerical methods due to their sheer volume and dimensional complexity. Research in data compression has demonstrated that large datasets can be effectively compressed, significantly reducing computational demands while maintaining reliable levels of accuracy. However, many existing approaches involve flattening tensorial data, which often compromises the intrinsic structure and meaning. This issue, combined with the pervasive curse of dimensionality and the loss of structural information when vectorizing or matricizing data, highlights the need for specialized low-rank tensor machine learning methods that preserve the data's inherent high dimensional structure and meaning.This work applies advanced computational techniques to tackle real-world challenges in data-driven modeling and prediction across diverse domains, including spatial wildfire modeling, wildfire remote-sensing data analysis, biochemical systems, and astrophysical simulations. By leveraging machine learning, tensor-based methods, stochastic modeling and a combination of machine learning and tensor-based methods, the study provides valuable insights into the behaviors of complex systems. These approaches not only enhance our understanding of intricate phenomena and underlying dynamics of complex systems but also enhance the accuracy and efficiency of predictive models, aid in decision-making processes, offering scalable and efficient solutions to problems in wildfire management, biochemical system simulation, and computational astrophysics.
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650 4 $a Astrophysics. $3 535904
650 4 $a Computational physics. $3 3343998
653 $a Machine learning
653 $a Tensors
653 $a Tensor-based methods
653 $a Soft margin classifiers
653 $a Lagrange multipliers
690 $a 0405
690 $a 0596
690 $a 0216
710 2 $a The University of Alabama. $b Mathematics. $e degree granting institution. $3 3821939
720 1 $a Sidje, Roger B. $e degree supervisor.
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791 $a Ph.D.
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