Support vector machine active learning with applications to text classification (2001)by S. Tong and D. Koller
[older version, 2000]
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.
S. Tong and D. Koller (2001). "Support vector machine active learning with applications to text classification." Journal of Machine Learning Research, 2, 45-66.
Full version of paper in ICML 2000.
author = "S. Tong and D. Koller",
title = "Support vector machine active learning with applications to text classification",
journal = "Journal of Machine Learning Research",
volume = 2,
pages = "45--66",
month = "November",
year = 2001,
note = "Full version of paper in ICML 2000",