Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26897
Title: Experimental study on parameter choices in norm-r support vector regression machines with noisy input
Authors: Wang, S
Zhu, J
Chung, FL 
Hu, D
Keywords: Support vector regression (SVR)
R-loss functions
Newton descent method
Issue Date: 2006
Publisher: Springer
Source: Soft computing, 2006, v. 10, no. 3, p. 219-223 How to cite?
Journal: Soft computing 
Abstract: In [1], with the evidence framework, the almost inversely linear dependency between the optimal parameter r in norm-r support vector regression machine r-SVR and the Gaussian input noise is theoretically derived. When r takes a non-integer value, r-SVR cannot be easily realized using the classical QP optimization method. This correspondence attempts to achieve two goals: (1) The Newton-decent-method based implementation procedure of r-SVR is presented here; (2) With this procedure, the experimental studies on the dependency between the optimal parameter r in r-SVR and the Gaussian noisy input are given. Our experimental results here confirm the theoretical claim in [1].
URI: http://hdl.handle.net/10397/26897
ISSN: 1432-7643
DOI: 10.1007/s00500-005-0474-z
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