An improved analysis of the Rademacher data-dependent bound using its self bounding property.
Identifieur interne : 000613 ( Main/Exploration ); 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
Affiliations:
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Le document en format XML
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Luca.Oneto@unige.it</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova</wicri:regionArea><wicri:noRegion>I-16145 Genova</wicri:noRegion></affiliation></author><author><name sortKey="Ghio, Alessandro" sort="Ghio, Alessandro" uniqKey="Ghio A" first="Alessandro" last="Ghio">Alessandro Ghio</name></author><author><name sortKey="Anguita, Davide" sort="Anguita, Davide" uniqKey="Anguita D" first="Davide" last="Anguita">Davide Anguita</name></author><author><name sortKey="Ridella, Sandro" sort="Ridella, Sandro" uniqKey="Ridella S" first="Sandro" last="Ridella">Sandro Ridella</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="doi">10.1016/j.neunet.2013.03.017</idno><idno type="RBID">pubmed:23587720</idno><idno type="pmid">23587720</idno><idno type="wicri:Area/PubMed/Corpus">000228</idno><idno type="wicri:Area/PubMed/Curation">000228</idno><idno type="wicri:Area/PubMed/Checkpoint">000234</idno><idno type="wicri:Area/Ncbi/Merge">000A76</idno><idno type="wicri:Area/Ncbi/Curation">000A76</idno><idno type="wicri:Area/Ncbi/Checkpoint">000A76</idno><idno type="wicri:Area/Main/Merge">000606</idno><idno type="wicri:Area/Main/Curation">000613</idno><idno type="wicri:Area/Main/Exploration">000613</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An improved analysis of the Rademacher data-dependent bound using its self bounding property.</title><author><name sortKey="Oneto, Luca" sort="Oneto, Luca" uniqKey="Oneto L" first="Luca" last="Oneto">Luca Oneto</name><affiliation wicri:level="1"><nlm:affiliation>DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova, Italy. Luca.Oneto@unige.it</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova</wicri:regionArea><wicri:noRegion>I-16145 Genova</wicri:noRegion></affiliation></author><author><name sortKey="Ghio, Alessandro" sort="Ghio, Alessandro" uniqKey="Ghio A" first="Alessandro" last="Ghio">Alessandro Ghio</name></author><author><name sortKey="Anguita, Davide" sort="Anguita, Davide" uniqKey="Anguita D" first="Davide" last="Anguita">Davide Anguita</name></author><author><name sortKey="Ridella, Sandro" sort="Ridella, Sandro" uniqKey="Ridella S" first="Sandro" last="Ridella">Sandro Ridella</name></author></analytic><series><title level="j">Neural networks : the official journal of the International Neural Network Society</title><idno type="e-ISSN">1879-2782</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial Intelligence</term><term>Statistics as Topic (methods)</term><term>Statistics as Topic (trends)</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Statistics as Topic</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Statistics as Topic</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Artificial Intelligence</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><affiliations><list><country><li>Italie</li></country></list><tree><noCountry><name sortKey="Anguita, Davide" sort="Anguita, Davide" uniqKey="Anguita D" first="Davide" last="Anguita">Davide Anguita</name><name sortKey="Ghio, Alessandro" sort="Ghio, Alessandro" uniqKey="Ghio A" first="Alessandro" last="Ghio">Alessandro Ghio</name><name sortKey="Ridella, Sandro" sort="Ridella, Sandro" uniqKey="Ridella S" first="Sandro" last="Ridella">Sandro Ridella</name></noCountry><country name="Italie"><noRegion><name sortKey="Oneto, Luca" sort="Oneto, Luca" uniqKey="Oneto L" first="Luca" last="Oneto">Luca Oneto</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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