Support Vector Machine Active Learning with Applications to Text Classification

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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:	720 - 5 self

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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