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Learning Classification with Both Labeled and Unlabeled Data
[chapter]
2002
Lecture Notes in Computer Science
A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of handlabeled examples. Labeling large amount of data is a costly process which in many cases is prohibitive. In this paper we show how the use of a small number of labeled data together with a large number of unlabeled data can create high-accuracy classifiers. Our approach does not rely on any parametric assumptions about the data as it is usually the case with generative
doi:10.1007/3-540-36755-1_39
fatcat:tfbjd3q5onacxfzp6qfhxnqoqi