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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.
Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing.Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high.
The papers of this volume are organized in topical sections on wired and wireless communication systems, high... Læs mere
This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations.
This comprehensive volume investigates the untapped potential of machine learning in educational settings. It examines the profound impact machine learning can have on reshaping educational research.