Transductive support vector machines for structured variables
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Proceedings of the 24th international conference on Machine learning. New York: Association for Computing Machinery, Inc, 2007. S. 1183-1190.
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Transductive support vector machines for structured variables
AU - Zien, Alexander
AU - Brefeld, Ulf
AU - Scheffer, Tobias
PY - 2007
Y1 - 2007
N2 - We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.
AB - We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=34547990643&partnerID=8YFLogxK
U2 - 10.1145/1273496.1273645
DO - 10.1145/1273496.1273645
M3 - Article in conference proceedings
AN - SCOPUS:34547990643
SN - 978-1-59593-793-3
SP - 1183
EP - 1190
BT - Proceedings of the 24th international conference on Machine learning
PB - Association for Computing Machinery, Inc
CY - New York
T2 - ACM International Conference Proceeding Series - AICPS 2007
Y2 - 20 June 2007 through 24 June 2007
ER -