Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Herrmann, Moritz"'
Autor:
Wünsch, Milena, Herrmann, Moritz, Noltenius, Elisa, Mohr, Mattia, Morris, Tim P., Boulesteix, Anne-Laure
Comparison studies in methodological research are intended to compare methods in an evidence-based manner, offering guidance to data analysts to select a suitable method for their application. To provide trustworthy evidence, they must be carefully d
Externí odkaz:
http://arxiv.org/abs/2408.11594
Autor:
Herrmann, Moritz, Lange, F. Julian D., Eggensperger, Katharina, Casalicchio, Giuseppe, Wever, Marcel, Feurer, Matthias, Rügamer, David, Hüllermeier, Eyke, Boulesteix, Anne-Laure, Bischl, Bernd
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we
Externí odkaz:
http://arxiv.org/abs/2405.02200
Autor:
Wünsch, Milena, Sauer, Christina, Herrmann, Moritz, Hinske, Ludwig Christian, Boulesteix, Anne-Laure
Gene set analysis, a popular approach for analysing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditions. In addition to the multitude of methods available for this task, u
Externí odkaz:
http://arxiv.org/abs/2402.00754
Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. The central aspects of separabilit
Externí odkaz:
http://arxiv.org/abs/2310.12806
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: theoretical arguments and empirical evidence show that clustering embed
Externí odkaz:
http://arxiv.org/abs/2207.00510
Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure of a data set. Our approach rests on the manifold assumption, i.e., t
Externí odkaz:
http://arxiv.org/abs/2207.00367
Autor:
Furrer, Cédric, Sieh, Daniel, Jank, Anne-Marie, Le Bras, Grégoire, Herrmann, Moritz, Reguant-Closa, Alba, Nemecek, Thomas
Publikováno v:
In Journal of Cleaner Production 10 September 2024 470
Autor:
Herrmann, Moritz, Scheipl, Fabian
We consider functional outlier detection from a geometric perspective, specifically: for functional data sets drawn from a functional manifold which is defined by the data's modes of variation in amplitude and phase. Based on this manifold, we develo
Externí odkaz:
http://arxiv.org/abs/2109.06849
Autor:
Nießl, Christina, Herrmann, Moritz, Wiedemann, Chiara, Casalicchio, Giuseppe, Boulesteix, Anne-Laure
Publikováno v:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12(2) (2022), e1441
In recent years, the need for neutral benchmark studies that focus on the comparison of methods from computational sciences has been increasingly recognised by the scientific community. While general advice on the design and analysis of neutral bench
Externí odkaz:
http://arxiv.org/abs/2106.02447
Autor:
Herrmann, Moritz, Scheipl, Fabian
In recent years, manifold methods have moved into focus as tools for dimension reduction. Assuming that the high-dimensional data actually lie on or close to a low-dimensional nonlinear manifold, these methods have shown convincing results in several
Externí odkaz:
http://arxiv.org/abs/2012.11987