Stability Analysis and Learning Bounds for Transductive Regression Algorithms
نویسندگان
چکیده
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.
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ورودعنوان ژورنال:
- CoRR
دوره abs/0904.0814 شماره
صفحات -
تاریخ انتشار 2009