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Semi-Supervised Learning Literature Survey - 2005

Semi-Supervised Learning Literature Survey

Research Area:  Machine Learning


In this survey, we focus on semi-supervised classification. It is a special form of classification. Traditional classifiers use only labeled data to train. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice.


Author(s) Name:  Xiaojin Zhu

Journal name:  University of Wisconsin – Madison - Technical Report

Conferrence name:  

Publisher name:  Computer Sciences, University of Wisconsin-Madison


Volume Information:  1530