Please use this identifier to cite or link to this item:
Christmann, Andreas
Steinwart, Ingo
Hubert, Mia
Year of Publication: 
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2006,03
Some methods from statistical machine learning and from robust statistics have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets, say with millions of data points. Secondly, robust and non-parametric confidence intervals for the predictions according to the fitted models are often unknown. Here, we propose a simple but general method to overcome these problems in the context of huge data sets. The method is scalable to the memory of the computer, can be distributed on several processors if available, and can help to reduce the computation time substantially. Our main focus is on robust general support vector machines (SVM) based on minimizing regularized risks. The method offers distribution-free confidence intervals for the median of the predictions. The approach can also be helpful to fit robust estimators in parametric models for huge data sets.
Breakdown point
convex risk minimization
data mining
distributed computing
influence function
logistic regression
Document Type: 
Working Paper

Files in This Item:
293.78 kB

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