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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]
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: Engineering Source
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  Data: 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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  Data: <i>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.</i> (Copyright applies to all Abstracts.)
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