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Support Vector Machine Active Learning with Applications to Text Classification (2001)

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by Simon Tong , Daphne Koller
Venue:JOURNAL OF MACHINE LEARNING RESEARCH
Citations:734 - 5 self
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BibTeX

@INPROCEEDINGS{Tong01supportvector,
    author = {Simon Tong and Daphne Koller},
    title = {Support Vector Machine Active Learning with Applications to Text Classification},
    booktitle = {JOURNAL OF MACHINE LEARNING RESEARCH},
    year = {2001},
    pages = {45--66},
    publisher = {}
}

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Abstract

Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

Keyphrases

text classification    support vector machine active learning    support vector machine    unlabeled instance    pool-based active learning    many setting    active learning    transductive setting    training set    version space    standard inductive    significant success    theoretical motivation    active learning method    labeled training instance    present experimental result    new algorithm    numerous real-world learning task   

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