Name
Abstract | ||
|---|---|---|
Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other traditional single hidden layer feedforward neural networks, such as Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Least Squares-Support Vector Regression (LS-SVR). Furthermore, ESVR has much faster learning speed than SVR and LS-SVR. Stabilities and robustnesses of these algorithms are also studied in the paper, which shows that the ESVR is more robust and stable. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patrec.2014.04.016 | Pattern Recognition Letters |
Keywords | Field | DocType |
Extreme Support Vector Regression,Extreme Support Vector Machine,Extreme support vectors,Extreme Learning Machine | Structured support vector machine,Kernel (linear algebra),Feedforward neural network,Pattern recognition,Least squares support vector machine,Extreme learning machine,Support vector machine,Robust regression,Artificial intelligence,Relevance vector machine,Mathematics | Journal |
Volume | ISSN | Citations |
45 | 0167-8655 | 1 |
PageRank | References | Authors |
0.37 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Zhu | 1 | 250 | 19.35 |
| Jun Miao | 2 | 220 | 22.17 |
| Laiyun Qing | 3 | 337 | 24.66 |