Empirical investigation of active learning strategies

Many predictive tasks require labeled data to induce classification models. The data labeling process may have a high cost. Several strategies have been proposed to optimize the selection of the most relevant examples, a process referred to as active learning. However, a lack of empirical studies co...

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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 326-327; S. 15 - 27
Hauptverfasser:	Pereira-Santos, Davi, Prudêncio, Ricardo Bastos Cavalcante, de Carvalho, André C.P.L.F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 31.01.2019
Schlagworte:	Active learning Agnostic active learning Data labeling Data sampling Non-agnostic active learning Partially labeled data Partially labeled data Non-agnostic active learning Agnostic active learning Data labeling Active learning Data sampling
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
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