title: An improved analysis of the Rademacher data-dependent bound using its self bounding property.
abstract: The problem of assessing the performance of a classifier, in the finite-sample setting, has been addressed by Vapnik in his seminal work by using data-independent measures of complexity. Recently, several authors have addressed the same problem by proposing data-dependent measures, which tighten previous results by taking in account the actual data distribution. In this framework, we derive some data-dependent bounds on the generalization ability of a classifier by exploiting the Rademacher Complexity and recent concentration results: in addition of being appealing for practical purposes, as they exploit empirical quantities only, these bounds improve previously known results.
authors.affiliation: DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova, Italy. Luca.Oneto@unige.it
authors.forename: Luca
authors.initials: L
authors.lastname: Oneto
authors.forename: Alessandro
authors.initials: A
authors.lastname: Ghio
authors.forename: Davide
authors.initials: D
authors.lastname: Anguita
authors.forename: Sandro
authors.initials: S
authors.lastname: Ridella
mesh_terms: D001185:Artificial Intelligence; D013223:Statistics as Topic / Q000379:methods / Q000639:trends*