%0 Journal Article %1 Tong2001 %A Tong, Simon %A Koller, Daphne %D 2001 %I JMLR.org %J J. Mach. Learn. Res. %K %P 45--66 %R http://dx.doi.org/10.1162/153244302760185243 %T Support vector machine active learning with applications to text classification %V 2 %X 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 <em>pool-based active learning</em>. 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 <em>version space</em>. 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.

@article{Tong2001, 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 <em>pool-based active learning</em>. 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 <em>version space</em>. 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.}, added-at = {2012-07-13T11:59:55.000+0200}, author = {Tong, Simon and Koller, Daphne}, biburl = {https://www.bibsonomy.org/bibtex/2a03d7c004b15c4cc3f07c1658ab43254/jabreftest}, doi = {http://dx.doi.org/10.1162/153244302760185243}, file = {Tong2001.pdf:2001/Tong2001.pdf:PDF}, groups = {public}, interhash = {b97045a365d311059ad803c9653f184f}, intrahash = {a03d7c004b15c4cc3f07c1658ab43254}, issn = {1532-4435}, journal = {J. Mach. Learn. Res.}, keywords = {}, pages = {45--66}, publisher = {JMLR.org}, timestamp = {2012-07-13T11:59:55.000+0200}, title = {Support vector machine active learning with applications to text classification}, username = {jabreftest}, volume = 2, year = 2001 }