Semi-Supervised Learning [Adaptive Computation and Machine Learning series] , ,

Semi-Supervised Learning [Adaptive Computation and Machine Learning series] , ,

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Semi-Supervised Learning [Adaptive Computation and Machine Learning series]

Semi-supervised learning is a powerful technique in the field of machine learning that combines labeled and unlabeled data to improve the performance of predictive models. In this book, the authors provide a comprehensive overview of semi-supervised learning, covering both theoretical foundations and practical applications.

The book starts with an introduction to semi-supervised learning, explaining the key concepts and motivations behind this approach. It then delves into the various algorithms and methods used in semi-supervised learning, including self-training, co-training, and graph-based techniques.

Throughout the book, the authors emphasize the importance of adaptability in semi-supervised learning, highlighting how models can be continuously updated and improved as new data becomes available. They also discuss the challenges and limitations of semi-supervised learning, and provide strategies for overcoming them.

Overall, this book is a valuable resource for researchers, practitioners, and students interested in leveraging unlabeled data to enhance the performance of machine learning models. It offers a comprehensive and up-to-date overview of semi-supervised learning, making it an essential addition to the Adaptive Computation and Machine Learning series.
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