Multi-view learning with dependent views

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Multi-view algorithms, such as co-training and co-EM, utilize unlabeled data when the available attributes can be split into independent and compatible subsets. Experiments have shown that multi-view learning is sometimes beneficial for problems for which the independence assumption is not satis fied. In practice, unfortunately, it is not possible to measure the dependency between two attribute sets; hence, there is no criterion which allows to decide whether multi-view learning is applicable. We conduct experiments with various text classification problems and investigate on the effectiveness of the co-trained SVM and the co-EM SVM under various conditions, including violations of the independence assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning. Copyright is held by the owner/author(s).

OriginalspracheEnglisch
Titel2015 Symposium on Applied Computing, SAC 2015 : Proceedings of the 30th Annual ACM Symposium on Applied Computing
HerausgeberDongwan Shin
Anzahl der Seiten6
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum13.04.2015
Seiten865-870
ISBN (Print)978-1-4503-3196-8
ISBN (elektronisch)9781450331968
DOIs
PublikationsstatusErschienen - 13.04.2015
Extern publiziertJa
Veranstaltung30th Annual ACM Symposium on Applied Computing - SAC 2015 - Salamanca, Spanien
Dauer: 13.04.201517.04.2015
Konferenznummer: 30
https://www.sigapp.org/sac/sac2015/

DOI

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