Performance of Extreme Learning Machine
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0169-8_13
Reference17 articles.
1. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006). https://doi.org/10.1016/j.neucom.2005.12.126
2. Zhao, Z., Zhang, X.: Theory and numerical analysis of extreme learning machine and its application for different degrees of defect recognition of hoisting wire rope. Shock Vib. (2018). https://doi.org/10.1155/2018/4168209
3. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B.: Evolutionary extreme learning machine. Pattern Recognit. 38(10), 1759–1763 (2005). https://doi.org/10.1016/j.patcog.2005.03.028
4. Cao, J., Lin, Z., Huang, G.B.: Composite function wavelet neural networks with differential evolution and extreme learning machine. Neural. Process. Lett. 33(3), 251–265 (2011). https://doi.org/10.1007/s11063-011-9176-y
5. Bacanin, N., Stoean, C., Zivkovic, M., Jovanovic, D., Antonijevic, M., Mladenovic, D.: Multi-swarm algorithm for extreme learning machine optimization. Sensors 22(11), 1–34 (2022). https://doi.org/10.3390/s22114204
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