Journalartikel - Wissenschaftlicher Artikel | 2026 | Peer reviewed

Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage

Autor*innen

Gerz, Fabian ; Jelali, Mohieddine

Zeitschrift

Smart agricultural technology

DOI

10.1016/j.atech.2026.102462

Weitere Details

Sprache

englisch

Darstellungsform

Text

Artikelnummer

102462

Zitierstile

Gerz, F., & Jelali, M. (2026). Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart Agricultural Technology, 102462. doi:10.1016/j.atech.2026.102462

In die Zwischenablage kopieren

Gerz, F. and Jelali, M., 2026. Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart agricultural technology, p.102462.

In die Zwischenablage kopieren

Gerz, Fabian & Jelali, Mohieddine 2026. Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart agricultural technology 102462.

In die Zwischenablage kopieren

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Zitierstile

Gerz, F., & Jelali, M. (2026). Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart Agricultural Technology, 102462. doi:10.1016/j.atech.2026.102462

In die Zwischenablage kopieren

Gerz, F. and Jelali, M., 2026. Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart agricultural technology, p.102462.

In die Zwischenablage kopieren

Gerz, Fabian & Jelali, Mohieddine 2026. Towards Reliable Data Augmentation in Machine Learning: Practices to Prevent Data Leakage. Smart agricultural technology 102462.

In die Zwischenablage kopieren

Download
RDF/XML-Format
JSON-LD-Format
Turtle-Format
N3-Format

American Psychological Association 6th edition (DOI)

Anglia Ruskin University - Harvard

Gesellschaft für Bildung und Forschung in Europa - Harvard