Multi-view Discriminative Sequential Learning

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Multi-view Discriminative Sequential Learning
Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.
Ulf Brefeld, Christoph Büscher, Tobias Scheff
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ECML
Authors Ulf Brefeld, Christoph Büscher, Tobias Scheffer
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