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Autor:
Brefeld, Ulf:

Titel:
Multi-view learning with dependent views

Quelle:
In: ACM (Hrsg.): Proceedings of the ACM/SIGAPP Symposium on Applied Computing New York : Association for Computing Machinery (2015) , 1-6

URL des Volltextes:
https://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/sac2015.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Algorithmus, Computerprogramm, Daten, Klassifikation, Lernen, Text


Abstract(original):
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 satisfied. 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 0assumption. We identify the error correlation coefficient of the initial classifiers as an elaborate indicator of the expected benefit of multi-view learning. (DIPF/Orig.)


DIPF-Abteilung:
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zuletzt verändert: 11.11.2016