Wed, 20 Aug 2008 22:05:51 BST CiteULike: davidr Scheffer http://www.citeulike.org/user/davidr/author/Scheffer CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/davidr/article/2719131 <i>(2004), pp. 19-26.</i> Multi-View Clustering Steffen Bickel Tobias Scheffer (2004), pp. 19-26. 2008-04-25T17:47:05-00:00 2004 19 26 IEEE Computer Society clustering multiview http://www.citeulike.org/user/davidr/article/1100007 <i>(2006), pp. 137-144.</i> Efficient co-regularised least squares regression. Ulf Brefeld Thomas Gärtner Tobias Scheffer Stefan Wrobel (2006), pp. 137-144. 2007-02-11T06:08:21-00:00 2006 137 144 ACM bibtex-import http://www.citeulike.org/user/davidr/article/1099774 <i>Vol. 23 (2006)</i> Efficient Co-Regularised Least Squares Regression Ulf Brefeld Thomas Gärtner Tobias Scheffer Stefan Wrobel Vol. 23 (2006) 2007-02-11T02:04:20-00:00 2006 23 bibtex-import cotraining multiview http://www.citeulike.org/user/davidr/article/572494 <i>(2005)</i><br /><br />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. Multi-View Discriminative Sequential Learning Ulf Brefeld Christoph Buscher Tobias Scheffer (2005) 2006-04-01T21:22:38-00:00 2005 cotraining semisupervised sequence-models