Name
Abstract | ||
|---|---|---|
Extreme learning machine (ELM) has shown its good performance in regression applications with a very fast speed. But there is still a difficulty to compromise between better generalization performance and smaller complexity of the ELM (a number of hidden nodes). This paper proposes a method called Delta Test-ELM (DT-ELM), which operates in an incremental way to create less complex ELM structures and determines the number of hidden nodes automatically. It uses Bayesian Information Criterion (BIC) as well as Delta Test (DT) to restrict the search as well as to consider the size of the network and prevent overfitting. Moreover, ensemble modeling is used on different DT-ELM models and it shows good test results in Experiments section. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2013.08.041 | Neurocomputing |
Keywords | Field | DocType |
good test result,delta test,good performance,hidden node,ensemble delta test-extreme,delta test-elm,experiments section,better generalization performance,complex elm structure,bayesian information criterion,different dt-elm model,ensemble modeling | Bayesian information criterion,Ensemble forecasting,Pattern recognition,Regression,Extreme learning machine,Incremental learning,Artificial intelligence,Overfitting,Ensemble learning,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
129, | 0925-2312 | 15 |
PageRank | References | Authors |
0.56 | 24 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Yu | 1 | 110 | 4.89 |
| Mark Van Heeswijk | 2 | 349 | 12.35 |
| Yoan Miche | 3 | 1054 | 54.56 |
| Rui Nian | 4 | 159 | 12.18 |
| Bo He | 5 | 43 | 13.55 |
| Eric Séverin | 6 | 155 | 9.17 |
| Amaury Lendasse | 7 | 1876 | 126.03 |