Comparative Study of Optimization Methods Proposed for the Extreme Learning Machine (ELM)
Author:
Affiliation:
1. University of Sousse, National Engineering School of Sousse,Sousse,Tunisia
2. Technische Universität Chemnitz,Measurement and Sensor Technology,Chemnitz,Germany
Funder
German Research Foundation ‘DFG’ (Deutsche Forschungsgemeinschaft)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9711751/9711675/09711902.pdf?arnumber=9711902
Reference8 articles.
1. Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease
2. Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application
3. A new pruning method for extreme learning machines via genetic algorithms
4. Bat algorithm for multi-objective optimisation;yang;International Journal of Bio-Inspired Computation,2011
5. OP-ELM: Optimally Pruned Extreme Learning Machine
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