Evolutionary extreme learning machine

✍ Scribed by Qin-Yu Zhu; A.K. Qin; P.N. Suganthan; Guang-Bin Huang

• Publisher: Elsevier Science
• Year: 2005
• Tongue: English
• Weight: 175 KB
• Volume: 38
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2005.03.028

✦ Synopsis

Extreme learning machine (ELM) [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25–29 July 2004], a novel learning algorithm much faster than the traditional gradient-based learning algorithms, was proposed recently for single-hidden-layer feedforward neural networks (SLFNs). However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. In this paper, a hybrid learning algorithm is proposed which uses the differential evolutionary algorithm to select the input weights and Moore–Penrose (MP) generalized inverse to analytically determine the output weights. Experimental results show that this approach is able to achieve good generalization performance with much more compact networks.