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Practical application of machine learning for analyses of biological matrices and environmental phenomena.


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Title: Practical application of machine learning for analyses of biological matrices and environmental phenomena.
Authors: Walsh, Alexandra
E-mail: alexandra.walsh@chem.gu.se
Issue Date: 11-Sep-2020
University: Göteborgs universitet. Naturvetenskapliga fakulteten
Institution: Department of Marine Sciences ; Institutionen för marina vetenskaper
Date of Defence: 2020-10-02
Disputation: Fredagen den 2 oktober 2020, kl. 10.00, Hörsal, Carl Skottsbergsgata 22B
Degree: Doctor of Philosophy
Publication type: Doctoral thesis
Keywords: Machine learning
Surface enhanced Raman spectroscopy
Acute lymphatic leukaemia
Volatile halogenated organic carbons
Marine algae
Doxorubicin
Principal component analysis
Multivariate statistics
Design of experiments
Waterlogged archaelogical wood
Transposed orthogonal partial least squares
Abstract: This thesis presents research aimed at forwarding an understanding of machine learning methods as a method of studying complex matrices and environmental phenomena. A number of machine learning methods in the form of linear projection algorithms and statistical experimental designs were applied for qualitative analysis of different matrices. The used linear projection algorithms included principal component analysis (PCA), partial least squares (PLS), orthogonal partial least squares (OPLS), and... more
ISBN: 978-91-8009-023-0
URI: http://hdl.handle.net/2077/66075
Appears in Collections:Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet
Doctoral Theses / Doktorsavhandlingar Institutionen för marina vetenskaper

 

 

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