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Transductive support vector machines for structured variables

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

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. S. 1183-1190.

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

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, S. 1183-1190, ACM International Conference Proceeding Series - AICPS 2007, Corvallis, USA / Vereinigte Staaten, 20.06.07. https://doi.org/10.1145/1273496.1273645

APA

Zien, A., Brefeld, U., & Scheffer, T. (2007). Transductive support vector machines for structured variables. In Proceedings of the 24th international conference on Machine learning (S. 1183-1190). Association for Computing Machinery, Inc. https://doi.org/10.1145/1273496.1273645

Vancouver

Zien A, Brefeld U, Scheffer T. Transductive support vector machines for structured variables. in Proceedings of the 24th international conference on Machine learning. New York: Association for Computing Machinery, Inc. 2007. S. 1183-1190 doi: 10.1145/1273496.1273645

Bibtex

@inbook{c375a4a2808f41c88ba1ccae9a2df760,
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.",
keywords = "Informatics, Business informatics",
author = "Alexander Zien and Ulf Brefeld and Tobias Scheffer",
year = "2007",
doi = "10.1145/1273496.1273645",
language = "English",
isbn = "978-1-59593-793-3",
pages = "1183--1190",
booktitle = "Proceedings of the 24th international conference on Machine learning",
publisher = "Association for Computing Machinery, Inc",
address = "United States",
note = "ACM International Conference Proceeding Series - AICPS 2007, AICPS ; Conference date: 20-06-2007 Through 24-06-2007",

}

RIS

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 -

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DOI

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