Introduction to semi-supervised learning

• Main Author: Zhu, Xiaojin.
• Other Authors: Goldberg, Andrew B.
• Format: Electronic
• Language: English
• Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2009.
• Series: Synthesis lectures on artificial intelligence and machine learning (Online), # 6.
• Subjects: Supervised learning (Machine learning); Support vector machines.
• Online Access: Abstract with links to full text

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) wh...

Table of Contents:

• Introduction to statistical machine learning
• The data
• Unsupervised learning
• Supervised learning
• Overview of semi-supervised learning
• Learning from both labeled and unlabeled data
• How is semi-supervised learning possible
• Inductive vs. transductive semi-supervised learning
• Caveats
• Self-training models
• Mixture models and EM
• Mixture models for supervised classification
• Mixture models for semi-supervised classification
• Optimization with the EM algorithm
• The assumptions of mixture models
• Other issues in generative models
• Cluster-then-label methods
• Co-training
• Two views of an instance
• Co-training
• The assumptions of co-training
• Multiview learning
• Graph-based semi-supervised learning
• Unlabeled data as stepping stones
• The graph
• Mincut
• Harmonic function
• Manifold regularization
• The assumption of graph-based methods
• Semi-supervised support vector machines
• Support vector machines
• Semi-supervised support vector machines
• Entropy regularization
• The assumption of S3VMS and entropy regularization
• Human semi-supervised learning
• From machine learning to cognitive science
• Study one: humans learn from unlabeled test data
• Study two: presence of human semi-supervised learning in a simple task
• Study three: absence of human semi-supervised learning in a complex task
• Discussions
• Theory and outlook
• A simple PAC bound for supervised learning
• A simple PAC bound for semi-supervised learning
• Future directions of semi-supervised learning
• Basic mathematical reference
• Semi-supervised learning software
• Symbols
• Biography.