Bibliographic Details
| Title: |
Efficient Computation and Model Selection for the Support Vector Regression. |
| Authors: |
Gunter, Lacey, Ji Zhu |
| Source: |
Neural Computation. Jun2007, Vol. 19 Issue 6, p1633-1655. 23p. |
| Subjects: |
Regression analysis, Multivariate analysis, Algorithms, Parameters (Statistics), Selection theorems |
| Abstract: |
In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR).We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter. [ABSTRACT FROM AUTHOR] |
|
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Psychology and Behavioral Sciences Collection |
