Transductive support vector machines for structured variables

Alexander Zien; Ulf Brefeld; Tobias Scheffer

doi:10.1145/1273496.1273645

Transductive support vector machines for structured variables

Alexander Zien^*
, Ulf Brefeld
, Tobias Scheffer

^*Corresponding author for this work

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

23 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 24th international conference on Machine learning
Number of pages	8
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Publication date	2007
Pages	1183-1190
ISBN (Print)	978-1-59593-793-3
DOIs	https://doi.org/10.1145/1273496.1273645
Publication status	Published - 2007
Externally published	Yes
Event	ACM International Conference Proceeding Series - AICPS 2007 - Corvallis, United States Duration: 20.06.2007 → 24.06.2007

Research areas and keywords

Informatics
Business informatics

Access to Document

10.1145/1273496.1273645

Cite this

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title = "Transductive support vector machines for structured variables",

abstract = "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.",

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author = "Alexander Zien and Ulf Brefeld and Tobias Scheffer",

year = "2007",

doi = "10.1145/1273496.1273645",

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booktitle = "Proceedings of the 24th international conference on Machine learning",

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Zien, A, Brefeld, U & Scheffer, T 2007, Transductive support vector machines for structured variables. in Proceedings of the 24th international conference on Machine learning. Association for Computing Machinery, Inc, New York, pp. 1183-1190, ACM International Conference Proceeding Series - AICPS 2007, Corvallis, United States, 20.06.07. https://doi.org/10.1145/1273496.1273645

Transductive support vector machines for structured variables. / Zien, Alexander; Brefeld, Ulf; Scheffer, Tobias.
Proceedings of the 24th international conference on Machine learning. New York: Association for Computing Machinery, Inc, 2007. p. 1183-1190.

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

TY - CHAP

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AU - Zien, Alexander

AU - Brefeld, Ulf

AU - Scheffer, Tobias

PY - 2007

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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 - https://www.scopus.com/pages/publications/34547990643

U2 - 10.1145/1273496.1273645

DO - 10.1145/1273496.1273645

M3 - Article in conference proceedings

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SN - 978-1-59593-793-3

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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 -

Transductive support vector machines for structured variables

Abstract

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