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Integrating machine learning approaches into network science: exemplary applications and novel algorithms

URN to cite this document: urn:nbn:de:bvb:355-epub-199125

Blöchl, Florian (2011) Integrating machine learning approaches into network science: exemplary applications and novel algorithms. PhD, Universität Regensburg

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Abstract (English)

The goal of this PhD thesis is to exemplify how methods to model complex systems, mainly the language of complex network science, and machine learning approaches can profit from each other. Thereby it deals with several projects arising from concrete questions to different complex systems from multiple fields of science. An introductory chapter explains important clustering algorithms and blind ...

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Translation of the abstract (German)

Das Ziel dieser Doktorarbeit ist es, beispielhaft zu zeigen, wie Methoden zur Modellierung komplexer Systeme - vor Allem die Sprache komplexer Netzwerke - und maschinelle Lernansätze voneinander profitieren können. Dabei behandelt sie mehrere Projekte, die aus konkreten Fragen an verschiedene komplexe Systeme aus unterschiedlichen wissenschaftlichen Disziplinen entspringen. Ein einführendes ...

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Export bibliographical data

Item Type:Thesis of the University of Regensburg (PhD)
Date:4 March 2011
Referee:Prof. Dr. Elmar W. Lang and Prof. Dr. Dr. Fabian J. Theis and Prof. Dr. Ingo Morgenstern
Date of exam:14 February 2011
Additional information (public):
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang > Arbeitsgruppe Dr. Fabian Theis
Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Research groups and research centres:Not selected
Keywords:complex networks, machine learning, matrix factorization, source separation, centrality measures, Input-Output network, effective parameters, microarray data analysis, graph-delayed correlation, GraDe, community detection, k-partite network, latent causes
Subjects:500 Science > 530 Physics
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Owner: Universitätsbibliothek Regensburg
Deposited On:04 Mar 2011 15:52
Last Modified:13 Mar 2014 17:01
Item ID:19912
Owner Only: item control page
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