Pool-Based Sequential Active Learning for Regression

Active learning (AL) is a machine-learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential AL for...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 30; no. 5; pp. 1348 - 1359
Main Author:	Wu, Dongrui
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active learning Active learning (AL) Colleges & universities Computational modeling Domains inductive learning Labeling Learning algorithms Machine learning passive sampling Performance enhancement Predictive models ridge regression Sociology Statistics Training Training data transductive learning
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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