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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Dettagli Bibliografici

Autori principali:	Li, Jingjing (Autore), Zhu, Lei (Autore), Du, Zhekai (Autore)
Ente Autore:	SpringerLink (Online service)
Natura:	Elettronico eBook
Lingua:	inglese
Pubblicazione:	Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edizione:	1st ed. 2024.
Serie:	Machine Learning: Foundations, Methodologies, and Applications,
Soggetti:	Machine learning. Artificial intelligence > Data processing. Data mining. Distribution (Probability theory). Machine Learning. Data Science. Data Mining and Knowledge Discovery. Distribution Theory.
Accesso online:	https://doi.org/10.1007/978-981-97-1025-6
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