An improved analysis of the Rademacher data-dependent bound using its self bounding property.
Identifieur interne : 000613 ( Main/Curation ); précédent : 000612; suivant : 000614An improved analysis of the Rademacher data-dependent bound using its self bounding property.
Auteurs : Luca Oneto [Italie] ; Alessandro Ghio ; Davide Anguita ; Sandro RidellaSource :
- Neural networks : the official journal of the International Neural Network Society ; 2013.
English descriptors
- KwdEn :
- MESH :
- methods : Statistics as Topic.
- trends : Statistics as Topic.
- Artificial Intelligence.
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.
DOI: 10.1016/j.neunet.2013.03.017
PubMed: 23587720
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pubmed:23587720Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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