Stability of transductive regression algorithms

Corinna Cortes, Mehryar Mohri, Dmitry Pechyony, Ashish Rastogi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, both by using convexity and closed-form solutions. Our analysis helps compare the stability of these algorithms. It suggests that several existing algorithms might not be stable but prescribes a technique to make them stable. It also reports the results of experiments with local transductive regression demonstrating the benefit of our stability bounds for model selection, in particular for determining the radius of the local neighborhood used by the algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Pages176-183
Number of pages8
ISBN (Print)9781605582054
DOIs
StatePublished - 2008
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: Jul 5 2008Jul 9 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning

Other

Other25th International Conference on Machine Learning
CountryFinland
CityHelsinki
Period7/5/087/9/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

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