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On grouping and partitioning approaches in interpretable machine learning
On grouping and partitioning approaches in interpretable machine learning
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Herbinger, Julia
2023
English
Universitätsbibliothek der Ludwig-Maximilians-Universität München
Herbinger, Julia (2023): On grouping and partitioning approaches in interpretable machine learning. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics
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DOI: 10.5282/edoc.32952
URN: urn:nbn:de:bvb:19-329525

Item Type:Theses (Dissertation, LMU Munich)
Subjects:000 Computers, Information and General Reference
000 Computers, Information and General Reference > 004 Data processing computer science
Faculties:Faculty of Mathematics, Computer Science and Statistics
Language:English
Date of oral examination:5. December 2023
1. Referee:Bischl, Bernd
MD5 Checksum of the PDF-file:0a88a24d6dd0525ecdd346372eaf51f3
Signature of the printed copy:0001/UMC 30111
ID Code:32952
Deposited On:17. Jan 2024 10:41
Last Modified:17. Jan 2024 10:41
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