Multi-view discriminative sequential learning

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

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
OriginalspracheEnglisch
TitelMachine Learning: ECML 2005 : 16th European Conference on Machine Learning
Anzahl der Seiten12
VerlagSpringer Verlag
Erscheinungsdatum01.01.2005
Seiten60-71
ISBN (Print)978-3-540-29243-2
ISBN (elektronisch)978-3-540-31692-3
DOIs
PublikationsstatusErschienen - 01.01.2005
Extern publiziertJa
Veranstaltung16th European Conference on Machine Learning and 9th European Conference on Principles and Practice of Knowledge Discovery in Databases - ECML PKDD 2005 - Porto, Portugal
Dauer: 03.10.200507.10.2005
Konferenznummer: 16
http://www.inescporto.pt/~jgama/ecmlpkdd05/

DOI

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