Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/77141 
Year of Publication: 
2000
Series/Report no.: 
Technical Report No. 2000,53
Publisher: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
The minimum number of misclassifications achievable with affine hyper_ planes on a given set of labeled points is a key quantity in both statistics and computational learning theory. However, determining this quantity exactly is essentially NP_hard_ cf_ Höfgen, Simon and van Horn (1995.) Hence, there is a need to find reasonable approximation procedures. This paper compares three approaches to approximating the minimum number of misclassifications achievable with afine hyperplanes. The first approach is based on the regression depth method of Rousseeuw and Hubert (1999) in linear regression models. We compare the results of the regression depth method with the support vector machine approach proposed by Vapnik (1998) and a heuristic search algorithm.
Document Type: 
Working Paper

Files in This Item:
File
Size
383.62 kB
657.87 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.