Relationship between classifier performance and distributional complexity for small samples

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dc.contributor.advisorDougherty, Edward R.
dc.contributor.committeeMemberSerpedin, Erchin
dc.contributor.committeeMemberLiu, Jyh-Charn
dc.contributor.committeeMemberNarayanan, Krishna
dc.creatorAttoor, Sanju Nair
dc.date.accessioned2004-11-15T19:49:46Z
dc.date.available2004-11-15T19:49:46Z
dc.date.created2003-08
dc.date.issued2004-11-15
dc.description.abstractGiven a limited number of samples for classification, several issues arise with respect to design, performance and analysis of classifiers. This is especially so in the case of microarray-based classification. In this paper, we use a complexity measure based mixture model to study classifier performance for small sample problems. The motivation behind such a study is to determine the conditions under which a certain class of classifiers is suitable for classification, subject to the constraint of a limited number of samples being available. Classifier study in terms of the VC dimension of a learning machine is also discussed.en
dc.format.digitalOriginborn digitalen
dc.format.extent276697 bytesen
dc.format.extent53293 bytesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.identifier.urihttps://hdl.handle.net/1969.1/1201
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectclassifieren
dc.subjectsmall sampleen
dc.subjectdistributional complexityen
dc.subjectVC dimensionen
dc.titleRelationship between classifier performance and distributional complexity for small samplesen
dc.typeBooken
dc.typeThesisen
dc.type.genreElectronic Thesisen
dc.type.materialtexten
thesis.degree.departmentElectrical Engineeringen
thesis.degree.disciplineVocational Educationen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Scienceen

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