The performance of a new hybrid classifier based on boxes and nearest neighbors

Martin Anthony, Joel Ratsaby

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as the 'sample width' of the classifier increases.

Original languageEnglish
StatePublished - 1 Dec 2012
Externally publishedYes
EventInternational Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 - Fort Lauderdale, FL, United States
Duration: 9 Jan 201211 Jan 2012

Conference

ConferenceInternational Symposium on Artificial Intelligence and Mathematics, ISAIM 2012
Country/TerritoryUnited States
CityFort Lauderdale, FL
Period9/01/1211/01/12

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