  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 also shows that a number of
widely used transductive regression algorithms are in fact unstable. Finally,
it reports the results of experiments with local transductive regression
demonstrating the benefit of our stability bounds for model selection, for one
of the algorithms, in particular for determining the radius of the local
neighborhood used by the algorithm.
