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Dokumententyp
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Anzahl der Publikationen: 8
Hochschulschrift
Herrmann, Moritz
(2019):
Large-scale benchmark study of prediction methods using multi-omics data.
Masterarbeit,
Ludwig-Maximilians-Universität München
[PDF, 633kB]
[PDF, 633kB]
Zeitschriftenartikel
Herrmann, Moritz 
ORCID: https://orcid.org/0000-0002-4893-5812; Kazempour, Daniyal; Scheipl, Fabian ORCID: https://orcid.org/0000-0001-8172-3603 und Kröger, Peer
(2024):
Enhancing cluster analysis via topological manifold learning.
In: Data Mining and Knowledge Discovery, Bd. 38, Nr. 3: S. 840-887
[PDF, 7MB]
ORCID: https://orcid.org/0000-0002-4893-5812; Kazempour, Daniyal; Scheipl, Fabian ORCID: https://orcid.org/0000-0001-8172-3603 und Kröger, Peer
(2024):
Enhancing cluster analysis via topological manifold learning.
In: Data Mining and Knowledge Discovery, Bd. 38, Nr. 3: S. 840-887
[PDF, 7MB]
Herrmann, Moritz ORCID: https://orcid.org/0000-0002-4893-5812; Pfisterer, Florian und Scheipl, Fabian
(2023):
A geometric framework for outlier detection in high‐dimensional data.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 3
[PDF, 43MB]
ORCID: https://orcid.org/0000-0002-4893-5812; Pfisterer, Florian und Scheipl, Fabian
(2023):
A geometric framework for outlier detection in high‐dimensional data.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 3
[PDF, 43MB]
Herrmann, Moritz ORCID: https://orcid.org/0000-0002-4893-5812 und Scheipl, Fabian ORCID: https://orcid.org/0000-0001-8172-3603
(2021):
A Geometric Perspective on Functional Outlier Detection.
In: Stats, Bd. 4, Nr. 4: S. 971-1011
[PDF, 6MB]
ORCID: https://orcid.org/0000-0002-4893-5812 und Scheipl, Fabian ORCID: https://orcid.org/0000-0001-8172-3603
(2021):
A Geometric Perspective on Functional Outlier Detection.
In: Stats, Bd. 4, Nr. 4: S. 971-1011
[PDF, 6MB]
Herrmann, Moritz; Probst, Philipp; Hornung, Roman; Jurinovic, Vindi und Boulesteix, Anne-Laure
(2021):
Large-scale benchmark study of survival prediction methods using multi-omics data.
In: Briefings in Bioinformatics, Bd. 22, Nr. 3, bbaa167
Niessl, Christina; Herrmann, Moritz; Wiedemann, Chiara; Casalicchio, Giuseppe und Boulesteix, Anne-Laure
(2021):
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results.
In: Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, Bd. 12, Nr. 2, e1441
Herrmann, Moritz; Probst, Philipp; Hornung, Roman; Jurinovic, Vindi und Boulesteix, Anne-Laure
(2020):
Large-scale benchmark study of survival prediction methods using multi-omics data.
In: Briefings in Bioinformatics: S. 1-15
[PDF, 545kB]
Konferenzbeitrag
Herrmann, Moritz ORCID: https://orcid.org/0000-0002-4893-5812; Lange, F. Julian D.; Eggensperger, Katharina; Casalicchio, Giuseppe; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; Feurer, Matthias; Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Boulesteix, Anne-Laure und Bischl, Bernd
(Juli 2024):
Position: Why We Must Rethink Empirical Research in Machine Learning.
41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024.
In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research
Bd. 235
PMLR. S. 18228-18247
[PDF, 334kB]
ORCID: https://orcid.org/0000-0002-4893-5812; Lange, F. Julian D.; Eggensperger, Katharina; Casalicchio, Giuseppe; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; Feurer, Matthias; Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Boulesteix, Anne-Laure und Bischl, Bernd
(Juli 2024):
Position: Why We Must Rethink Empirical Research in Machine Learning.
41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024.
In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research
Bd. 235
PMLR. S. 18228-18247
[PDF, 334kB]