Temel, Turgay2021-03-202021-03-2020180013-51941350-911Xhttp://doi.org/10.1049/el.2018.0790https://hdl.handle.net/20.500.12885/789A 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.eninfo:eu-repo/semantics/closedAccesslearning (artificial intelligence)pattern classificationtext analysishigh-accuracy document classificationmodified proximity-measurepredetermined correct classification levelsliding-mode approachstable variationweight updatesPython librariestext classificationdocument categorisationlearning vector quantisation classifierArticle10.1049/el.2018.0790541710281029WOS:000441396400007Q3Q3