Multiple-relations-constrained image classification with limited training samples via Pareto optimization

It is a significant challenge to classify images using limited training samples. To this end, we formulate image classification as a multi-task multi-view (MTMV) learning problem and propose a novel Pareto optimization-based method to find the solution. Specifically, we first build a multi-objective...

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Veröffentlicht in:	Neural computing & applications Jg. 31; H. 10; S. 6821 - 6842
Hauptverfasser:	Zhou, Di, Wang, Jun, Jiang, Bin, Li, Yajun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.10.2019 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Classification Classifiers Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image classification Image Processing and Computer Vision Multiple objective analysis Optimization Original Article Pareto optimization Pareto optimum Probability and Statistics in Computer Science Training Multiple-relations-constrained Multi-objective optimization Image classification Multi-task multi-view learning
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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