Features

• Provides a systematic framework for semi-supervised learning, emphasizing its connection to other learning tasks
• Covers the theory and applications of semi-supervised learning
• Offers abundant examples of a variety of models, including generative models, semi-supervised SVMs, and graph-based semi-supervised learning methods
• Explores advances and future topics in semi-supervised learning
• Includes a toolbox of popular algorithms for fast and easy application

Summary

Including the historical background and recent advances in the field as well as theoretical perspectives and real-world applications, this book outlines a systematic framework for implementing semi-supervised learning methods. It provides a toolbox on semi-supervised learning algorithms, presenting illustrations and examples of each algorithm. The book defines and distinguishes supervised learning, unsupervised learning, semi-supervised learning, and other relevant learning tasks. It discusses important semi-supervised learning models, including generative models for semi-supervised learning, semi-supervised support vector machines, and graph-based semi-supervised learning methods.