Name
Abstract | ||
|---|---|---|
Besides the studied transition learning between the two different ensemble learning algorithms such as negative correlation learning and balanced ensemble learning, transition learning could also implemented in negative correlation learning with different correlation penalties. On one hand, negative correlation learning with the lower correlation penalty named as low negative correlation learning might learn too much the training data while generating less negatively correlated neural networks. On the other hand, negative correlation learning with the higher correlation penalty called as high negative correlation learning might not be able to learn the training data, but be capable of generating highly negatively correlated neural networks. By conducting transition learning from low negative correlation learning to high negative correlation learning, this paper shows that the ensembles could have both the good performance and the diverse individual neural networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889706 | IJCNN |
Keywords | Field | DocType |
low negative correlation learning,negatively correlated neural networks,learning (artificial intelligence),correlation penalties,balanced ensemble learning,transition learning,ensemble learning algorithms,high negative correlation learning,neural nets,neural networks,learning artificial intelligence,correlation,training data,computer science | Training set,Negative correlation,Pattern recognition,Computer science,Unsupervised learning,Correlation,Artificial intelligence,Generalization error,Artificial neural network,Ensemble learning,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 1 | 0.41 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Liu | 1 | 2526 | 265.08 |
| Qiangfu Zhao | 2 | 214 | 62.36 |
| Yan Pei | 3 | 125 | 22.89 |