Bibliographic Details
| Title: |
Leave-One-Out Bounds for Support Vector Regression Model Selection. |
| Authors: |
Ming-Wei Chang1 b6506056@csie.ntu.edu.tw, Chih-Jen Lin1 cjlin@csie.ntu.edu.tw |
| Source: |
Neural Computation. May2005, Vol. 17 Issue 5, p1188-1222. 35p. |
| Subjects: |
Vector processing (Computer science), Computer programming, Electronic data processing, Vector analysis, Computer software, Computers |
| Abstract: |
Minimizing bounds of leave-one-out errors is an important and efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this letter, we derive various leave-one-out bounds for support vector regression (SVR) and discuss the difference from those for classification. Experiments demonstrate that the proposed bounds are competitive with Bayesian SVR for parameter selection. We also discuss the differentiability of leave-one-out bounds. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
