Name
Title | ||
|---|---|---|
SVM training with duplicated samples and its application in SVM-based ensemble methods |
Abstract | ||
|---|---|---|
Support vector machine (SVM)-based ensemble techniques, such as bagging or boosting, generally involve training SVMs with duplicated samples. A simple derivation illustrates that the same result can be obtained solely by training on those unique samples if a SVM parameter is adjusted, which introduce a faster training algorithm, and provides insights into SVM-based ensemble methods. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.neucom.2004.04.004 | Neurocomputing |
Keywords | Field | DocType |
Support vector machines,Bagging,Boosting,Regularization parameter | Ranking SVM,Pattern recognition,Support vector machine,Boosting (machine learning),Artificial intelligence,Ensemble learning,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
61 | C | 0925-2312 |
Citations | PageRank | References |
1 | 0.45 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junshui Ma | 1 | 311 | 23.01 |
| Ashok Krishnamurthy | 2 | 455 | 56.47 |
| Stanley Ahalt | 3 | 10 | 0.98 |