文献
J-GLOBAL ID:202202257413972376
整理番号:22A0451145
プロトタイプベース分類機密のための量子触発学習ベクトル量子化器:ニューラルネットワークとアプリケーション5/2020への個人使用のための個人利用【JST・京大機械翻訳】
Quantum-inspired learning vector quantizers for prototype-based classification Confidential: for personal use only-submitted to Neural Networks and Applications 5/2020
著者 (5件):
Villmann Thomas
(Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Mittweida, Germany)
,
Engelsberger Alexander
(Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Mittweida, Germany)
,
Ravichandran Jensun
(Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Mittweida, Germany)
,
Villmann Andrea
(Berufliches Schulzentrum Dobeln-Mittweida, Schulteil Mittweida, Mittweida, Germany)
,
Kaden Marika
(Saxon Institute for Computational Intelligence and Machine Learning (SICIM), University of Applied Sciences Mittweida, Mittweida, Germany)
資料名:
Neural Computing & Applications
(Neural Computing & Applications)
巻:
34
号:
1
ページ:
79-88
発行年:
2022年
JST資料番号:
W0703A
ISSN:
0941-0643
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
ドイツ (DEU)
言語:
英語 (EN)