Semi-supervised learning for structured output variables
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Standard
Semi-supervised learning for structured output variables. / Brefeld, Ulf; Scheffer, Tobias.
Proceedings of the 23rd international conference on Machine learning. Hrsg. / William Cohen; Andrew Moore. Association for Computing Machinery, Inc, 2006. S. 145-152.Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - CHAP
T1 - Semi-supervised learning for structured output variables
AU - Brefeld, Ulf
AU - Scheffer, Tobias
N1 - Conference code: 23
PY - 2006/1/1
Y1 - 2006/1/1
N2 - 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.
AB - 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.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=34250753883&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/ddd5325c-60a2-384e-b277-453f35a64a0e/
U2 - 10.1145/1143844.1143863
DO - 10.1145/1143844.1143863
M3 - Article in conference proceedings
AN - SCOPUS:34250753883
SN - 978-1-59593-383-6
SP - 145
EP - 152
BT - Proceedings of the 23rd international conference on Machine learning
A2 - Cohen, William
A2 - Moore, Andrew
PB - Association for Computing Machinery, Inc
T2 - ICML '06
Y2 - 25 June 2006 through 29 June 2006
ER -