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Unsupervised Domain Adaptation: Recent Advances and Future Perspectives - Machine Learning: Foundations, Methodologies, and Applications Jingjing Li
Unsupervised Domain Adaptation: Recent Advances and Future Perspectives - Machine Learning: Foundations, Methodologies, and Applications
Jingjing Li
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.
| メディア | 書籍 Paperback Book (ソフトカバーで背表紙を接着した本) |
| リリース済み | 2025年4月23日 |
| ISBN13 | 9789819710270 |
| 出版社 | Springer Verlag, Singapore |
| ページ数 | 223 |
| 寸法 | 150 × 220 × 10 mm · 371 g |