title: Fully Empirical and Data-Dependent Stability-Based Bounds.
abstract: The purpose of this paper is to obtain a fully empirical stability-based bound on the generalization ability of a learning procedure, thus, circumventing some limitations of the structural risk minimization framework. We show that assuming a desirable property of a learning algorithm is sufficient to make data-dependency explicit for stability, which, instead, is usually bounded only in an algorithmic-dependent way. In addition, we prove that a well-known and widespread classifier, like the support vector machine (SVM), satisfies this condition. The obtained bound is then exploited for model selection purposes in SVM classification and tested on a series of real-world benchmarking datasets demonstrating, in practice, the effectiveness of our approach.
authors.forename: Luca
authors.initials: L
authors.lastname: Oneto
authors.forename: Alessandro
authors.initials: A
authors.lastname: Ghio
authors.forename: Sandro
authors.initials: S
authors.lastname: Ridella
authors.forename: Davide
authors.initials: D
authors.lastname: Anguita