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dc.contributor.authorMcClellan, Richard Paul, 1944-
dc.creatorMcClellan, Richard Paul, 1944-en_US
dc.date.accessioned2013-05-02T09:35:56Z
dc.date.available2013-05-02T09:35:56Z
dc.date.issued1971en_US
dc.identifier.urihttp://hdl.handle.net/10150/287814
dc.language.isoen_USen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectPattern perception -- Mathematical models.en_US
dc.titleOPTIMIZATION AND STOCHASTIC APPROXIMATION TECHNIQUES APPLIED TO UNSUPERVISED LEARNINGen_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.identifier.oclc27759450en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.identifier.proquest7209186en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.namePh.D.en_US
dc.identifier.bibrecord.b26111305en_US
refterms.dateFOA2018-06-30T17:50:39Z


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