Batch Mode Active Learning for Regression With Expected Model Change

While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that resu...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2017-07, Vol.28 (7), p.1668-1681
Hauptverfasser:	Cai, Wenbin, Zhang, Muhan, Zhang, Ya
Format:	Artikel
Sprache:	eng
Schlagworte:	Active learning Active learning (AL) Algorithm design and analysis Algorithms Approximation algorithms batch mode Classification Computer simulation Context modeling Data models Datasets expected model change Learning Linear regression Machine learning algorithms Mathematical models nonlinear regression Nonlinearity Probability theory Query processing Regression Training
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