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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Bibliographic Details
Published in:	Mathematics (Basel) Vol. 10; no. 1; p. 39
Main Authors:	Huang, Qihang, He, Yulin, Huang, Zhexue
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2022
Subjects:	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
Online Access:	Get full text
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