Extreme Learning Machine With Affine Transformation Inputs in an Activation Function

The extreme learning machine (ELM) has attracted much attention over the past decade due to its fast learning speed and convincing generalization performance. However, there still remains a practical issue to be approached when applying the ELM: the randomly generated hidden node parameters without...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 30; číslo 7; s. 2093 - 2107
Hlavní autori:	Cao, Jiuwen, Zhang, Kai, Yong, Hongwei, Lai, Xiaoping, Chen, Badong, Lin, Zhiping
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Affine transformation (AT) activation function Affine transformations Algorithms Approximation algorithms Artificial neural networks Benchmark testing Benchmarks classification Datasets Distributed databases Entropy extreme learning machine (ELM) Image classification Learning algorithms Learning systems Maximum entropy Object recognition Parameters regression Robustness (mathematics) Scaling Training Tuning
ISSN:	2162-237X, 2162-2388, 2162-2388
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