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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...
Compared with conventional weighted voting methods, class-specific soft voting (CSSV) system has several advantages. On one hand, it not only deals with the soft class probability outputs but also refines the weights from classifiers to classes. On the other hand, the class-specific weights can be used to improve the combinative performance without increasing much computational load. This paper proposes...
Extreme learning machine (ELM), as an emergent technique for training feed-forward neural networks, has shown good performances on various learning domains. This paper investigates the impact of random weights during the training of ELM. It focuses on the randomness of weights between input and hidden layers, and the dimension change from input layer to hidden layer. The direct motivation is to verify...
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