Discriminative transfer learning via local and global structure preservation

The current success of supervised learning is limited on large amounts of labeled training data. Transfer learning aims to learn an adaptive classifier for the unlabeled target domain data from the labeled source domain data, which is sampled from diverse probability distributions under changing con...

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Veröffentlicht in:	Signal, image and video processing image and video processing, 2019-06, Vol.13 (4), p.753-760
Hauptverfasser:	Wang, Chao, Tuo, Hongya, Wang, Jiexin, Qiao, Lingfeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Imaging Computer Science Data transfer (computers) Domains Image Processing and Computer Vision Multimedia Information Systems Original Paper Pattern Recognition and Graphics Preservation Regularization Signal,Image and Speech Processing Supervised learning Vision
Online-Zugang:	Volltext
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