A fast RBM-hidden-nodes based extreme learning machine
| Autor: | Chao Sun, HaiPeng Xi, Li Chen, Ling Yang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Restricted Boltzmann machine
Training set Generalization Computer science business.industry 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Computational learning theory 0202 electrical engineering electronic engineering information engineering Probability distribution 020201 artificial intelligence & image processing Artificial intelligence business computer Extreme learning machine |
| Zdroj: | 2017 29th Chinese Control And Decision Conference (CCDC). |
| DOI: | 10.1109/ccdc.2017.7978866 |
| Popis: | In this paper, we propose an Extreme Learning Machine (ELM) approach for solving large and complex data problems. In contrast to existing approaches, we embed hidden nodes that are designed using Restricted Boltzmann machine (RBM) into the classical ELM, exhibiting excellent generalization performances. To overcome the high computational complexity involved especially on large datasets, hidden nodes are derived from RBM trained in turn by multiple random subsets of data sampled from the original datasets instead of the entire dataset in one time. The resultant algorithm proposed is labeled here as FRBM-H-ELM in short. Comprehensive experiments and comparisons are conducted to assess the FRBM-H-ELM against the traditional Extreme Learning Machine. The results obtained demonstrated the superior generalization performance and efficiency of FRBM-H-ELM. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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