Learning from Labeled and Unlabeled Data using Graph Mincuts

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Learning from Labeled and Unlabeled Data using Graph Mincuts (2001)

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by Avrim Blum , Shuchi Chawla

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334 - 5 self

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@MISC{Blum01learningfrom,
    author = {Avrim Blum and Shuchi Chawla},
    title = {Learning from Labeled and Unlabeled Data using Graph Mincuts},
    year = {2001}
}

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Abstract

Many application domains suffer from not having enough labeled training data for learning. However, large amounts of unlabeled examples can often be gathered cheaply. As a result, there has been a great deal of work in recent years on how unlabeled data can be used to aid classification. We consider an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data. Our algorithm

Keyphrases

graph mincuts unlabeled example training data unlabeled data many application domain recent year minimum cut great deal pairwise relationship large amount

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