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Efficient co-regularised least squares regression

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

Standard

Efficient co-regularised least squares regression. / Brefeld, Ulf; Gärtner, Thomas; Scheffer, Tobias et al.

Proceedings of the 23rd international conference on Machine learning. Hrsg. / William Cohen. Association for Computing Machinery, Inc, 2006. S. 137-144 (ACM International Conference Proceeding Series; Band 148).

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

Harvard

Brefeld, U, Gärtner, T, Scheffer, T & Wrobel, S 2006, Efficient co-regularised least squares regression. in W Cohen (Hrsg.), Proceedings of the 23rd international conference on Machine learning. ACM International Conference Proceeding Series, Bd. 148, Association for Computing Machinery, Inc, S. 137-144, International Conference on Machine Learning - ICML 2006, Pittsburgh, USA / Vereinigte Staaten, 25.06.06. https://doi.org/10.1145/1143844.1143862

APA

Brefeld, U., Gärtner, T., Scheffer, T., & Wrobel, S. (2006). Efficient co-regularised least squares regression. in W. Cohen (Hrsg.), Proceedings of the 23rd international conference on Machine learning (S. 137-144). (ACM International Conference Proceeding Series; Band 148). Association for Computing Machinery, Inc. https://doi.org/10.1145/1143844.1143862

Vancouver

Brefeld U, Gärtner T, Scheffer T, Wrobel S. Efficient co-regularised least squares regression. in Cohen W, Hrsg., Proceedings of the 23rd international conference on Machine learning. Association for Computing Machinery, Inc. 2006. S. 137-144. (ACM International Conference Proceeding Series). doi: 10.1145/1143844.1143862

Bibtex

@inbook{d224cc66882a4795951b471f850457f5,
title = "Efficient co-regularised least squares regression",
abstract = "In many applications, unlabelled examples are inexpensive and easy to obtain. Semi-supervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semi-supervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Ex-periments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.",
keywords = "Informatics, Business informatics",
author = "Ulf Brefeld and Thomas G{\"a}rtner and Tobias Scheffer and Stefan Wrobel",
year = "2006",
month = jan,
day = "1",
doi = "10.1145/1143844.1143862",
language = "English",
isbn = "978-159593383-6",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery, Inc",
pages = "137--144",
editor = "William Cohen",
booktitle = "Proceedings of the 23rd international conference on Machine learning",
address = "United States",
note = "ICML '06 ; Conference date: 25-06-2006 Through 29-06-2006",

}

RIS

TY - CHAP

T1 - Efficient co-regularised least squares regression

AU - Brefeld, Ulf

AU - Gärtner, Thomas

AU - Scheffer, Tobias

AU - Wrobel, Stefan

N1 - Conference code: 23

PY - 2006/1/1

Y1 - 2006/1/1

N2 - In many applications, unlabelled examples are inexpensive and easy to obtain. Semi-supervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semi-supervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Ex-periments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.

AB - In many applications, unlabelled examples are inexpensive and easy to obtain. Semi-supervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semi-supervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Ex-periments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=34250767770&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/48577d33-c292-3153-b395-46acf9d90df8/

U2 - 10.1145/1143844.1143862

DO - 10.1145/1143844.1143862

M3 - Article in conference proceedings

AN - SCOPUS:34250767770

SN - 978-159593383-6

SN - 1595933832

T3 - ACM International Conference Proceeding Series

SP - 137

EP - 144

BT - Proceedings of the 23rd international conference on Machine learning

A2 - Cohen, William

PB - Association for Computing Machinery, Inc

T2 - ICML '06

Y2 - 25 June 2006 through 29 June 2006

ER -

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DOI