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A data-dependent skeleton estimate and a scale-sensitive dimension for classification

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dc.contributor.author Horvath, Marta
dc.contributor.author Lugosi, Gábor
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.accessioned 2017-07-26T10:39:07Z
dc.date.available 2017-07-26T10:39:07Z
dc.date.issued 1996-12-01
dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=199
dc.identifier.citation Discrete Applied Mathematics, 86, (1998), pp. 37-61
dc.identifier.uri http://hdl.handle.net/10230/1073
dc.description.abstract The classical binary classification problem is investigated when it is known in advance that the posterior probability function (or regression function) belongs to some class of functions. We introduce and analyze a method which effectively exploits this knowledge. The method is based on minimizing the empirical risk over a carefully selected ``skeleton'' of the class of regression functions. The skeleton is a covering of the class based on a data--dependent metric, especially fitted for classification. A new scale--sensitive dimension is introduced which is more useful for the studied classification problem than other, previously defined, dimension measures. This fact is demonstrated by performance bounds for the skeleton estimate in terms of the new dimension.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries Economics and Business Working Papers Series; 199
dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title A data-dependent skeleton estimate and a scale-sensitive dimension for classification
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2017-07-23T02:02:53Z
dc.subject.keyword estimation
dc.subject.keyword hypothesis testing
dc.subject.keyword statistical decision theory: operations research
dc.subject.keyword Statistics, Econometrics and Quantitative Methods
dc.rights.accessRights info:eu-repo/semantics/openAccess

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