Experimental study on parameter choices in norm-r support vector regression machines with noisy input

S. Wang, J. Zhu, Fu Lai Korris Chung, Hu Dewen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

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].
Original languageEnglish
Pages (from-to)219-223
Number of pages5
JournalSoft Computing
Volume10
Issue number3
DOIs
Publication statusPublished - 1 Feb 2006

Keywords

  • Newton descent method
  • r-loss functions
  • Support vector regression (SVR)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

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