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Efficient Computation and Model Selection for the Support Vector Regression.

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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
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PubType: Academic Journal
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  Data: Efficient Computation and Model Selection for the Support Vector Regression.
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  Data: <searchLink fieldCode="AR" term="%22Gunter%2C+Lacey%22">Gunter, Lacey</searchLink><br /><searchLink fieldCode="AR" term="%22Ji+Zhu%22">Ji Zhu</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Jun2007, Vol. 19 Issue 6, p1633-1655. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parameters+%28Statistics%29%22">Parameters (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+theorems%22">Selection theorems</searchLink>
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  Data: 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]
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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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1162/neco.2007.19.6.1633
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1633
    Subjects:
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Parameters (Statistics)
        Type: general
      – SubjectFull: Selection theorems
        Type: general
    Titles:
      – TitleFull: Efficient Computation and Model Selection for the Support Vector Regression.
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            NameFull: Gunter, Lacey
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            NameFull: Ji Zhu
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          Dates:
            – D: 01
              M: 06
              Text: Jun2007
              Type: published
              Y: 2007
          Identifiers:
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              Value: 08997667
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              Value: 19
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              Value: 6
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            – TitleFull: Neural Computation
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