Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection

Autor: Yu, Jinyang, Hamdan, Sami, Sasse, Leonard, Morrison, Abigail, Patil, Kaustubh R.

Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirica

Externí odkaz: http://arxiv.org/abs/2311.14079

Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models

Autor: Hamdan, Sami, More, Shammi, Sasse, Leonard, Komeyer, Vera, Patil, Kaustubh R., Raimondo, Federico

The fast-paced development of machine learning (ML) methods coupled with its increasing adoption in research poses challenges for researchers without extensive training in ML. In neuroscience, for example, ML can help understand brain-behavior relati

Externí odkaz: http://arxiv.org/abs/2310.12568

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity.

Autor: Sasse, Leonard^1,2,3 (AUTHOR), Larabi, Daouia I.^1,2 (AUTHOR), Omidvarnia, Amir^1,2 (AUTHOR), Jung, Kyesam^1,2 (AUTHOR), Hoffstaedter, Felix^1,2 (AUTHOR), Jocham, Gerhard⁴ (AUTHOR), Eickhoff, Simon B.^1,2 (AUTHOR), Patil, Kaustubh R.^1,2 (AUTHOR) k.patil@fz-juelich.de

Publikováno v: Communications Biology. 7/10/2023, Vol. 6 Issue 1, p1-14. 14p.

Neuroimaging-Genetics bridge for better understanding of human brain organization

Autor: Demirtas, Yasir, Sasse, Leonard, Abdik, Ecehan, Omidvarnia, Amir, Raimondo, Federico, Cakir, Tunahan, Patil, Kaustubh

Publikováno v: Helmholtz Imaging Conference 2022, Berlin, Germany, 2022-05-31-2022-06-01

Magnetic Resonance Imaging (MRI) has promising potential for clinical translation due to its non-invasive nature and high spatial resolution. MRI offers structural and functional measurement modalities which capture the fundamental principles of huma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3364::4176a34f940a8b44771a177c4498c465
https://hdl.handle.net/2128/33136

Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models.

Autor: Hamdan S; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany., More S; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany., Sasse L; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany.; Max Planck School of Cognition, Stephanstrasse 1a, Leipzig, Germany., Komeyer V; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany., Patil KR; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany., Raimondo F; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany.; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Germany.

Publikováno v: GigaByte (Hong Kong, China) [GigaByte] 2024 Mar 07; Vol. 2024, pp. gigabyte113. Date of Electronic Publication: 2024 Mar 07 (Print Publication: 2024).