Olivier Chapelle, Bernhard Schoelkopf & Alexander Zien"Semi-Supervised Learning"
MIT Press | ISBN:0262033585 | 2006. | 524 p. | RARed | PDF | 5.39MB
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 first comprehensive 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…
