Context Out Classifier
| dc.contributor.author | Hrebik, Radek | |
| dc.contributor.author | Kukal, Jaromir | |
| dc.coverage.issue | 1 | cs |
| dc.coverage.volume | 24 | cs |
| dc.date.accessioned | 2019-06-26T10:18:36Z | |
| dc.date.available | 2019-06-26T10:18:36Z | |
| dc.date.issued | 2018-06-01 | cs |
| dc.description.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. | en |
| dc.format | text | cs |
| dc.format.extent | 101-106 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Mendel. 2018 vol. 24, č. 1, s. 101-106. ISSN 1803-3814 | cs |
| dc.identifier.doi | 10.13164/mendel.2018.1.101 | en |
| dc.identifier.issn | 2571-3701 | |
| dc.identifier.issn | 1803-3814 | |
| dc.identifier.uri | http://hdl.handle.net/11012/179230 | |
| dc.language.iso | en | cs |
| dc.publisher | Institute of Automation and Computer Science, Brno University of Technology | cs |
| dc.relation.ispartof | Mendel | cs |
| dc.relation.uri | https://mendel-journal.org/index.php/mendel/article/view/29 | cs |
| dc.rights.access | openAccess | en |
| dc.subject | classification | en |
| dc.subject | binary programming | en |
| dc.subject | cluster union | en |
| dc.subject | imperfect learning | en |
| dc.title | Context Out Classifier | en |
| dc.type.driver | article | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.faculty | Fakulta strojního inženýrství | cs |
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