Support Vector Regression
IR@CIMFR: CSIR-Central Institute of Mining and Fuel Research, Dhanbad
View Archive Info| Field | Value | |
| Title |
Support Vector Regression
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| Creator |
Basak, D.
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| Subject |
Electrical Testing
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| Description |
Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized
performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear
function. SVR has been applied in various fields – time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.
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| Date |
2007
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://cimfr.csircentral.net/38/1/1.pdf
Basak, D. (2007) Support Vector Regression. International Journal of Neural Information Processing – Letters and Reviews, 11 (10). pp. 203-224. |
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| Relation |
http://cimfr.csircentral.net/38/
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