Resource title

Robust Learning from Bites

Resource image

image for OpenScout resource :: Robust Learning from Bites

Resource description

Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the fitted models are often unknown. Here, we propose a general method to overcome these problems of robust estimation 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. The method additionally offers distribution-free confidence intervals for the median of the predictions. The method is illustrated for two situations: robust estimation in linear regression and kernel logistic regression from statistical machine learning.

Resource author

Andreas Christmann

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22598

Resource license

Adapt according to the presented license agreement and reference the original author.