Novelty Detection and Online Learning for Chunk Data Streams

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. Th...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2021-07, Vol.43 (7), p.2400-2412
Hauptverfasser:	Wang, Yi, Ding, Yi, He, Xiangjian, Fan, Xin, Lin, Chi, Li, Fengqi, Wang, Tianzhu, Luo, Zhongxuan, Luo, Jiebo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification Computer Science Computer Science, Artificial Intelligence Data models Data stream Data transmission Distance learning Engineering Engineering, Electrical & Electronic Fans Feature extraction feature selection Hilbert space Kernel Kernels Linear systems Machine learning novelty detection online learning Science & Technology Streaming media Technology
Online-Zugang:	Volltext bestellen
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