Stress Measures in SOM Learning
| dc.contributor.author | Krbcova, Zuzana | |
| 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 | Various stress measures can be used in generalized version of Sammon’s mapping. Kohonen SOM with iterative or batch learning is a standard tool for data self-organization, too. Our method applies stress functions to pattern relationships in SOM and converts batch learning to discrete optimization task. Due to NP–completeness of SOM learning, optimization heuristics have to be used. Simulated annealing making use of Lévy flights is the recommended heuristics for this task. | en |
| dc.format | text | cs |
| dc.format.extent | 107-112 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Mendel. 2018 vol. 24, č. 1, s. 107-112. ISSN 1803-3814 | cs |
| dc.identifier.doi | 10.13164/mendel.2018.1.107 | en |
| dc.identifier.issn | 2571-3701 | |
| dc.identifier.issn | 1803-3814 | |
| dc.identifier.uri | http://hdl.handle.net/11012/179231 | |
| 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/30 | cs |
| dc.rights.access | openAccess | en |
| dc.subject | SOM | en |
| dc.subject | metric space | en |
| dc.subject | stress function | en |
| dc.subject | optimization heuristics | en |
| dc.title | Stress Measures in SOM Learning | 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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