Name
Abstract | ||
|---|---|---|
Kernel Ridge Regression (KRR) is a powerful nonlinear regression method. The combination of KRR and the truncated-regularized Newton method, which is based on the conjugate gradient (CG) method, leads to a powerful regression method. The proposed method (algorithm), is called Truncated-Regularized Kernel Ridge Regression (TR-KRR). Compared to the closed-form solution of KRR, Support Vector Machines (SVM) and Least-Squares Support Vector Machines (LS-SVM) algorithms on six data sets, the proposed TR-KRR algorithm is as accurate as, and much faster than all of the other algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.knosys.2014.08.012 | Knowledge-Based Systems |
Keywords | Field | DocType |
Regression,Least-squares,Kernel ridge regression,Kernel methods,Truncated Newton | Data mining,Mathematical optimization,Least squares support vector machine,Principal component regression,Computer science,Nonparametric regression,Support vector machine,Polynomial regression,Algorithm,Polynomial kernel,Kernel method,Kernel regression | Journal |
Volume | Issue | ISSN |
71 | 1 | 0950-7051 |
Citations | PageRank | References |
5 | 0.70 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maher Maalouf | 1 | 48 | 5.36 |
| Dirar Homouz | 2 | 34 | 4.00 |