Unsupervised Domain Adaptation Recent Advances and Future Perspectives /

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicab...

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Podrobná bibliografie

Hlavní autoři:	Li, Jingjing (Autor), Zhu, Lei (Autor), Du, Zhekai (Autor)
Korporativní autor:	SpringerLink (Online service)
Médium:	Elektronický zdroj E-kniha
Jazyk:	angličtina
Vydáno:	Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Vydání:	1st ed. 2024.
Edice:	Machine Learning: Foundations, Methodologies, and Applications,
Témata:	Machine learning. Artificial intelligence > Data processing. Data mining. Distribution (Probability theory). Machine Learning. Data Science. Data Mining and Knowledge Discovery. Distribution Theory.
On-line přístup:	https://doi.org/10.1007/978-981-97-1025-6
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!