Efficient co-regularised least squares regression

Ulf Brefeld; Thomas Gärtner; Tobias Scheffer; Stefan Wrobel

doi:10.1145/1143844.1143862

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 -

DOI

https://doi.org/10.1145/1143844.1143862
Endgültige, publizierte Fassung

Efficient co-regularised least squares regression

Standard

Harvard

APA

Vancouver

Bibtex

RIS

Weitere Publikationen dieser Person(en)

Interactive sequential generative models for team sports

Masked autoencoder for multiagent trajectories

Toward Learning Distributions of Distributions

Studying properties of water data using manifold-aware anomaly detectors

Convolutional Neural Networks

DOI

Zuletzt angesehen

Projekte

Publikationen