Co-Regularized Least-Squares for Label Ranking
Inhaltsverzeichnis
Reference
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes: Co-Regularized Least-Squares for Label Ranking. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 107-123.
DOI
http://dx.doi.org/10.1007/978-3-642-14125-6_6
Abstract
Situations when only a limited amount of labeled data and a large amount of unlabeled data are available to the learning algorithm are typical for many real-world problems. To make use of unlabeled data in preference learning problems, we propose a semisupervised algorithm that is based on the multiview approach. Our algorithm, which we call Sparse Co-RankRLS, minimizes a least-squares approximation of the ranking error and is formulated within the co-regularization framework. It operates by constructing a ranker for each view and by choosing such ranking prediction functions that minimize the disagreement among all of the rankers on the unlabeled data. Our experiments, conducted on real-world dataset, show that the inclusion of unlabeled data can improve the prediction performance significantly. Moreover, our semisupervised preference learning algorithm has a linear complexity in the number of unlabeled data items, making it applicable to large datasets.
Extended Abstract
Bibtex
@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_6},
title={Co-Regularized Least-Squares for Label Ranking},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_6, http://de.evo-art.org/index.php?title=Co-Regularized_Least-Squares_for_Label_Ranking },
publisher={Springer Berlin Heidelberg},
author={Tsivtsivadze, Evgeni and Pahikkala, Tapio and Boberg, Jorma and Salakoski, Tapio and Heskes, Tom},
pages={107-123},
language={English}
}
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Links
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Sonstige Links
http://www.learning-machines.com/ Evgeni Levin-Tsivtsivadze
