Training nu-Support Vector Regression: Theory and Algorithms.
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| Title: | Training nu-Support Vector Regression: Theory and Algorithms. |
|---|---|
| Authors: | Chang, Chih-Chung1, Lin, Chih-Jen2 |
| Source: | Neural Computation. Aug2002, Vol. 14 Issue 8, p1959-1977. 19p. |
| Subjects: | Vector processing (Computer science), Regression analysis, Decomposition method |
| Abstract: | We discuss the relation between ε-support vector regression (ε-SVR) and ν-support vector regression (ν-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of ε and the scaling of target values. A practical decomposition method for ν-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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