Semi-Supervised Learning

丛 书 名：Adaptive Computation and Machine Learning series

作     者：Olivier Chapelle Bernhard Scholkopf Alexander Zien 

I S B N：(纸本) 9780262033589 

出 版 社：The MIT Press 

出 版 年：2006年

页      数：524页

主 题 词：machine learning semi-supervised learning training examples label data application domains bioinformatics algorithms taxonomy low-density separation manifold structure 

摘      要：In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. It first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.