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dc.contributor.authorMcGraw, John M.en_US
dc.date.accessioned2014-03-14T20:47:01Z
dc.date.available2014-03-14T20:47:01Z
dc.date.issued2001-10-23en_US
dc.identifier.otheretd-10252001-104137en_US
dc.identifier.urihttp://hdl.handle.net/10919/35487
dc.description.abstractWe desire an algorithm to classify a physical object in ``real-time" using an easily portable probing device. The probe excites a given object at frequencies from 100 MHz up to 800 MHz at intervals of 0.5 MHz. Thus the data used for classification is the 1400-component vector of these frequency responses. The Interdisciplinary Center for Applied Mathematics (ICAM) was asked to help develop an algorithm and executable computer code for the probing device to use in its classification analysis. Due to these and other requirements, all work had to be done in Matlab. Hence a significant portion of the effort was spent in writing and testing applicable Matlab code which incorporated the various statistical techniques implemented. We offer three approaches to classification: maximum log-likelihood estimates, correlation coefficients, and confidence bands. Related work included considering ways to recover and exploit certain symmetry characteristics of the objects (using the response data). Present investigations are not entirely conclusive, but the correlation coefficient classifier seems to produce reasonable and consistent results. All three methods currently require the evaluation of the full 1400-component vector. It has been suggested that unknown portions of the vectors may include extraneous and misleading information, or information common to all classes. Identifying and removing the respective components may be beneficial to classification regardless of method. Another advantage of dimension reduction should be a strengthening of mean and covariance estimates.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartmcgrawjm.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectMultivariate Normalen_US
dc.subjectFrequency Responseen_US
dc.subjectConfidence Intervalen_US
dc.subjectData Analysisen_US
dc.subjectCorrelationen_US
dc.subjectMaximum Likelihooden_US
dc.titleAn Investigation into Classification of High Dimensional Frequency Dataen_US
dc.typeThesisen_US
dc.contributor.departmentStatisticsen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineStatisticsen_US
dc.contributor.committeechairSmith, Eric P.en_US
dc.contributor.committeememberWoodall, William H.en_US
dc.contributor.committeememberBurns, John A.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10252001-104137/en_US
dc.date.sdate2001-10-25en_US
dc.date.rdate2002-10-25
dc.date.adate2001-10-25en_US


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