Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11564096_11 | ECML |
Keywords | Field | DocType |
semi-supervised learning mechanism,semi-supervised hidden markov perceptron,semi-supervised hidden markov support,multi-view discriminative sequential learning,entity recognition,generative model,inexpensive unlabeled sequence,baum-welch algorithm,unlabeled data,information extraction,sequential data,semi supervised learning,discrimination learning,baum welch,support vector | Semi-supervised learning,Pattern recognition,Computer science,Supervised learning,Unsupervised learning,Artificial intelligence,Artificial neural network,Hidden Markov model,Discriminative model,Perceptron,Machine learning,Generative model | Conference |
Volume | ISSN | ISBN |
3720 | 0302-9743 | 3-540-29243-8 |
Citations | PageRank | References |
12 | 0.89 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulf Brefeld | 1 | 633 | 51.89 |
| Christoph Büscher | 2 | 17 | 1.44 |
| Tobias Scheffer | 3 | 1862 | 139.64 |