Online and Non-Parametric Drift Detection Methods Based on Hoeffding's Bounds

Incremental and online learning algorithms are more relevant in the data mining context because of the increasing necessity to process data streams. In this context, the target function may change overtime, an inherent problem of online learning (known as concept drift). In order to handle concept d...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2015-03, Vol.27 (3), p.810-823
Hauptverfasser:	Frias-Blanco, Isvani, Campo-Avila, Jose del, Ramos-Jimenez, Gonzalo, Morales-Bueno, Rafael, Ortiz-Diaz, Agustin, Caballero-Mota, Yaile
Format:	Artikel
Sprache:	eng
Schlagworte:	Data models Detectors Electronic mail Monitoring Random variables Vectors
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