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Computational Methods in Machine Lea...

Njeunje, Franck Olivier Ndjakou.

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Computational Methods in Machine Learning: Transport Model, Haar Wavelet, DNA Classification, and MRI.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Methods in Machine Learning: Transport Model, Haar Wavelet, DNA Classification, and MRI./
Author:	Njeunje, Franck Olivier Ndjakou.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	161 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-04, Section: B.
Contained By:	Dissertations Abstracts International80-04B.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10843333
ISBN:	9780438401969

Computational Methods in Machine Learning: Transport Model, Haar Wavelet, DNA Classification, and MRI.
Njeunje, Franck Olivier Ndjakou.

Computational Methods in Machine Learning: Transport Model, Haar Wavelet, DNA Classification, and MRI. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 161 p.

Source: Dissertations Abstracts International, Volume: 80-04, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2018.

This item must not be sold to any third party vendors.

With the increasing amount of raw data generation produced every day, it has become pertinent to develop new techniques for data representation, analyses, and interpretation. Motivated by real-world applications, there is a trending interest in techniques such as dimensionality reduction, wavelet decomposition, and classication methods that allow for better understanding of data. This thesis details the development of a new non-linear dimension reduction technique based on transport model by advection. We provide a series of computational experiments, and practical applications in hyperspectral images to illustrate the strength of our algorithm. In wavelet decomposition, we construct a novel Haar approximation technique for functions f in the Lp-space, 0 < p < 1, such that the approximants have support contained in the support of f. Furthermore, a classification algorithm to study tissue-specific deoxyribonucleic acids (DNA) is constructed using the support vector machine. In magnetic resonance imaging, we provide an extension of the T2-store-T2 magnetic resonance relaxometry experiment used in the analysis of magnetization signal from 2 to N exchanging sites, where N ≥ 2.

ISBN: 9780438401969Subjects--Topical Terms:

1669109
Applied Mathematics.

Computational Methods in Machine Learning: Transport Model, Haar Wavelet, DNA Classification, and MRI.
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500 $a Source: Dissertations Abstracts International, Volume: 80-04, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Czaja, Wojciech K.;Benedetto, John J.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
506 $a This item must not be sold to any third party vendors.
520 $a With the increasing amount of raw data generation produced every day, it has become pertinent to develop new techniques for data representation, analyses, and interpretation. Motivated by real-world applications, there is a trending interest in techniques such as dimensionality reduction, wavelet decomposition, and classication methods that allow for better understanding of data. This thesis details the development of a new non-linear dimension reduction technique based on transport model by advection. We provide a series of computational experiments, and practical applications in hyperspectral images to illustrate the strength of our algorithm. In wavelet decomposition, we construct a novel Haar approximation technique for functions f in the Lp-space, 0 < p < 1, such that the approximants have support contained in the support of f. Furthermore, a classification algorithm to study tissue-specific deoxyribonucleic acids (DNA) is constructed using the support vector machine. In magnetic resonance imaging, we provide an extension of the T2-store-T2 magnetic resonance relaxometry experiment used in the analysis of magnetization signal from 2 to N exchanging sites, where N ≥ 2.
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