Name
Title | ||
|---|---|---|
An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention. |
Abstract | ||
|---|---|---|
It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation (LOO-CV) is usually used to select a model with good generalization performance among potential candidates. The primary reason for using the LOO-CV is ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCYB.2015.2458177 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Training,Presses,Computer architecture,Reliability,Testing,Artificial neural networks,Approximation error | Data mining,Computer science,Extreme learning machine,Automation,Artificial intelligence,Artificial neural network,Mathematical optimization,Stability (learning theory),Test data,Generalization error,Cross-validation,Approximation error,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 8 | 2168-2267 |
Citations | PageRank | References |
3 | 0.38 | 42 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhifei Shao | 1 | 62 | 4.97 |
| J. Meng | 2 | 2793 | 174.51 |
| Ning Wang | 3 | 333 | 18.88 |