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- W2247372677 abstract "In this paper, we propose two stream-based active learning algorithms for single-hidden layer feed-forward neural networks (SLFNs) trained by extreme learning machine (ELM). Uncertainty and inconsistency are adopted as two sample selection criteria. Uncertainty reflects the nondeterminacy of a sample among different decision classes, which is calculated by information entropy or Gini-index. Inconsistency reflects the disagreement of the sample between its conditional features and decision labels, which is calculated by the lower approximations in fuzzy rough sets. Experimental results demonstrate that inconsistency-based strategy is more effective than uncertainty based strategy for SLFNs under stream-based environment." @default.
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- W2247372677 date "2015-10-01" @default.
- W2247372677 modified "2023-09-27" @default.
- W2247372677 title "Active Learning Based on Single-Hidden Layer Feed-Forward Neural Network" @default.
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- W2247372677 doi "https://doi.org/10.1109/smc.2015.377" @default.
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