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Context Out Classifier

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29-Article Text-72-1-10-20190218.pdf (521.6Kb)
Date
2018-06-01
Author
Hrebik, Radek
Kukal, Jaromir
Altmetrics
10.13164/mendel.2018.1.101
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Abstract
Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden classes we can build their unions using binary optimization task. Resulting system has the best possible sensitivity over all output classes. Another way is to perform second level linear classification as referential approach. The presented techniques are demonstrated on traditional iris flower task.
Keywords
classification, binary programming, cluster union, imperfect learning
Persistent identifier
http://hdl.handle.net/11012/179230
Document type
Peer reviewed
Document version
Final PDF
Source
Mendel. 2018 vol. 24, č. 1, s. 101-106. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/29
DOI
10.13164/mendel.2018.1.101
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  • Vol. 24, No. 1 [24]
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