Name
Abstract | ||
|---|---|---|
Ensemble learning has gained considerable attention in different learning tasks including regression, classification, and clustering problems. One of the drawbacks of the ensemble is the high computational cost of training stages. Resampling local negative correlation (RLNC) is a technique that combines two well-known methods to generate ensemble diversity—resampling and error negative correlation—and a fine-grain parallel approach that allows us to achieve a satisfactory balance between accuracy and efficiency. In this paper, we introduce a structure of the virtual machine aimed to test diverse selection strategies of parameters in neural ensemble designs, such as RLNC. We assess the parallel performance of this approach on a virtual machine cluster based on the full virtualization paradigm, using speedup and efficiency as performance metrics, for different numbers of processors and training data sizes. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s00521-011-0544-3 | Neural Computing and Applications |
Keywords | DocType | Volume |
behavior analysis,fine-grain parallel approach,neural network ensemble algorithm,different number,virtual machine cluster,different learning task,parallel performance,ensemble diversity,ensemble learning,ensemble learningartificial neural networks � virtualizationmulticore processorparallel algorithms,local negative correlation,neural ensemble design,error negative correlation,performance metrics | Journal | 21 |
Issue | ISSN | Citations |
3 | 1433-3058 | 2 |
PageRank | References | Authors |
0.35 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. Fernández | 1 | 35 | 8.76 |
| Carlos Valle | 2 | 21 | 8.20 |
| Francisco Saravia | 3 | 8 | 0.76 |
| Héctor Allende | 4 | 148 | 31.69 |