English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Experimentally optimal ν in support vector regression for different noise models and parameter settings

Chalimourda, A., Schölkopf, B., & Smola, A. (2004). Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural networks, 17(1), 127-141. doi:10.1016/S0893-6080(03)00209-0.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DA2F-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-4F9B-5
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Chalimourda, A, Author
Schölkopf, B1, 2, Author              
Smola, AJ, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: In Support Vector (SV) regression, a parameter ν controls the number of Support Vectors and the number of points that come to lie outside of the so-called var epsilon-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of ν that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression. As a side effect of the experiments, valuable information about the generalization behavior of the remaining SVM parameters and their dependencies is gained. The experimental findings are valid even for complex ‘real-world’ data sets. Based on our results on the role of the ν-SVM parameters, we discuss various model selection methods.

Details

show
hide
Language(s):
 Dates: 2004-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/S0893-6080(03)00209-0
BibTex Citekey: 4680
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Neural networks
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 17 (1) Sequence Number: - Start / End Page: 127 - 141 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496