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A Multi-View Learning Algorithm

Authors: Lazarova, Gergana; Chechev, Milen; Koychev, Ivan;

A Multi-View Learning Algorithm

Abstract

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014 Recently, there has been significant interest in multi-dimensional representation of the objects, searching for new features in new sources of information. Each view of the object represents a separate data source for learning a separate classifier. A multi-view teaching algorithm is compared to a single learner. Both models use Naïve Bayes Classifier for the underlying classifiers. The multi-view algorithm is especially applicable in areas where it is difficult to obtain the classifications of the examples. Association for the Development of the Information Society, Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Plovdiv University "Paisii Hilendarski"

Country
Bulgaria
Keywords

semi-supervised learning, classification, co-training

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green