PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stability of Transductive Regression Algorithms
Corinna Cortes, Mehryar Mohri, Dmitry Pechyony and Ashish Rastogi
In: ICML 2008, 5-9 July, 2008, Helsinki, Finland.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5225
Deposited By:Dmitry Pechyony
Deposited On:24 March 2009