Paper Info
Title
Model complexity control and statistical learning theory
Abstract
We discuss the problem of modelcomplexity control also known as modelselection. This problem frequently arises inthe context of predictive learning and adaptiveestimation of dependencies from finite data.First we review the problem of predictivelearning as it relates to model complexitycontrol. Then we discuss several issuesimportant for practical implementation ofcomplexity control, using the frameworkprovided by Statistical Learning Theory (orVapnik-Chervonenkis theory). Finally, we showpractical applications of Vapnik-Chervonenkis(VC) generalization bounds for model complexitycontrol. Empirical comparisons of differentmethods for complexity control suggestpractical advantages of using VC-based modelselection in settings where VC generalizationbounds can be rigorously applied. We also arguethat VC-theory provides methodologicalframework for complexity control even when itstechnical results can not be directly applied.
Year
DOI
Venue
2002
10.1023/A:1015007927558
Natural Computing
Keywords
DocType
Volume
complexity control,model selection,prediction risk,predictive learning,signal denoising,statistical learning theory,VC-generalization bounds,wavelet thresholding
Journal
1
Issue
ISSN
Citations 
1
1572-9796
8
PageRank 
References 
Authors
0.73
9
1
Name
Order
Citations
PageRank
Vladimir Cherkassky11064126.66