A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm

To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier using highly confident unlabeled samples. The MMD-...

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Vydané v:	Mathematics (Basel) Ročník 10; číslo 1; s. 39
Hlavní autori:	Huang, Qihang, He, Yulin, Huang, Zhexue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.01.2022
Predmet:	Active learning Algorithms Classification Classifiers Cluster analysis Clustering co-training Confidence Datasets distribution consistency Machine learning maximum mean discrepancy Methods Multilayer perceptrons Paradigms self-training Semi-supervised learning Statistical analysis Statistical methods Subject specialists Training Vector quantization
ISSN:	2227-7390, 2227-7390
On-line prístup:	Získať plný text
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