Semi-supervised learning for structured output variables

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

The problem of learning a mapping between input and structured, interdependent output variables covers sequential, spatial, and relational learning as well as predicting recursive structures. Joint feature representations of the input and output variables have paved the way to leveraging discriminative learners such as SVMs to this class of problems. We address the problem of semi-supervised learning in joint input output spaces. The cotraining approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semi-supervised support vector learning algorithm for joint input out-put spaces and arbitrary loss functions. Experiments investigate the benefit of semi-supervised structured models in terms of accuracy and F1 score.
OriginalspracheEnglisch
TitelProceedings of the 23rd international conference on Machine learning
HerausgeberWilliam Cohen, Andrew Moore
Anzahl der Seiten8
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum01.01.2006
Seiten145-152
ISBN (Print)978-1-59593-383-6
DOIs
PublikationsstatusErschienen - 01.01.2006
Extern publiziertJa
VeranstaltungInternational Conference on Machine Learning - ICML 2006 - Carnegie Mellon University, Pittsburgh, USA / Vereinigte Staaten
Dauer: 25.06.200629.06.2006
Konferenznummer: 23

DOI

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