A hybrid online sequential extreme learning machine with simplified hidden network
In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOS-ELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducin...
| Principais autores: | , , , |
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| Outros Autores: | |
| Formato: | Journal Article |
| Idioma: | English |
| Publicado em: |
2014
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/106191 http://hdl.handle.net/10220/23965 |
| Resumo: | In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOS-ELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducing the number of hidden nodes in Single-hidden Layer Feed-forward Neural Networks (SLFNs) with Radial Basis Function (RBF) by virtue of adjustment in node allocation and pruning capability. Simulation results show that the generalization performance of the proposed HOS-ELM is comparable to the original OS- ELM with significant reduction in the number of hidden nodes. |
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