High-accuracy document classification with a new algorithm
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Engineering Technology-Iet
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
learning (artificial intelligence), pattern classification, text analysis, high-accuracy document classification, modified proximity-measure, predetermined correct classification level, sliding-mode approach, stable variation, weight updates, Python libraries, text classification, document categorisation, learning vector quantisation classifier
Kaynak
Electronics Letters
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
54
Sayı
17
