High-accuracy document classification with a new algorithm
| dc.contributor.author | Temel, Turgay | |
| dc.date.accessioned | 2021-03-20T20:13:06Z | |
| dc.date.available | 2021-03-20T20:13:06Z | |
| dc.date.issued | 2018 | |
| dc.department | BTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | A new algorithm based on learning vector quantisation classifier is presented based on a modified proximity-measure, which enforces a predetermined correct classification level in training while using sliding-mode approach for stable variation in weight updates towards convergence. The proposed algorithm and some well-known counterparts are implemented by using Python libraries and compared in a task of text classification for document categorisation. Results reveal that the new classifier is a successful contender to those algorithms in terms of testing and training performances. | en_US |
| dc.identifier.doi | 10.1049/el.2018.0790 | en_US |
| dc.identifier.endpage | 1029 | en_US |
| dc.identifier.issn | 0013-5194 | |
| dc.identifier.issn | 1350-911X | |
| dc.identifier.issue | 17 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 1028 | en_US |
| dc.identifier.uri | http://doi.org/10.1049/el.2018.0790 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12885/789 | |
| dc.identifier.volume | 54 | en_US |
| dc.identifier.wos | WOS:000441396400007 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Temel, Turgay | |
| dc.language.iso | en | en_US |
| dc.publisher | Inst Engineering Technology-Iet | en_US |
| dc.relation.ispartof | Electronics Letters | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | learning (artificial intelligence) | en_US |
| dc.subject | pattern classification | en_US |
| dc.subject | text analysis | en_US |
| dc.subject | high-accuracy document classification | en_US |
| dc.subject | modified proximity-measure | en_US |
| dc.subject | predetermined correct classification level | en_US |
| dc.subject | sliding-mode approach | en_US |
| dc.subject | stable variation | en_US |
| dc.subject | weight updates | en_US |
| dc.subject | Python libraries | en_US |
| dc.subject | text classification | en_US |
| dc.subject | document categorisation | en_US |
| dc.subject | learning vector quantisation classifier | en_US |
| dc.title | High-accuracy document classification with a new algorithm | en_US |
| dc.type | Article | en_US |
