Adaptive Learning for Dynamic Features and Noisy Labels

Applying current machine learning algorithms in complex and open environments remains challenging, especially when different changing elements are coupled and the training data is scarce. For example, in the activity recognition task, the motion sensors may change position or fall off due to the int...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 47; H. 2; S. 1219 - 1237
Hauptverfasser:	Gu, Shilin, Xu, Chao, Hu, Dewen, Hou, Chenping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2025
Schlagworte:	Adaptation models Adaptive learning Data models Dynamic feature space Feature extraction heterogeneous model reuse Heuristic algorithms incremental learning Noise Noise measurement noisy labels Risk minimization Streams Training
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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