Name
Title | ||
|---|---|---|
Regularized Least Squares Twin SVR for the Simultaneous Learning of a Function and its Derivative |
Abstract | ||
|---|---|---|
In a recent publication, Lazaro et al. addressed the problem of simultaneously approximating a function and its derivative using support vector machines. In this paper, we propose a new approach termed as regularized least squares twin support vector regression, for the simultaneous learning of a function and its derivatives. The regressor is obtained by solving one of two related support vector machine-type problems, each of which is of a smaller size than the one obtained in Lazaro's approach. The proposed algorithm is simple and fast, as no quadratic programming problem needs to be solved. Effectively, only the solution of a pair of linear systems of equations is needed. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.246826 | IJCNN |
Keywords | Field | DocType |
quadratic programming,support vector regression,learning (artificial intelligence),regression analysis,support vector machines,function approximation,function simultaneous learning,least squares approximations,regularized least squares twin support vector regression,support vector machine-type problem,linear equation,quadratic programming problem,learning artificial intelligence,support vector machine,linear system of equations,quadratic program,least squares approximation | Pattern recognition,Function approximation,Linear system,Least squares support vector machine,Regularized least squares,Regression analysis,Support vector machine,Artificial intelligence,Quadratic programming,Mathematics,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7803-9490-9 | 1 |
PageRank | References | Authors |
0.35 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jayadeva | 1 | 788 | 38.14 |
| Reshma Khemchandani | 2 | 304 | 20.42 |
| Suresh Chandra | 3 | 902 | 48.57 |