Graduate Thesis Or Dissertation

 

Application of pattern recognition to a human behavior problem Public Deposited

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  • Pattern recognition techniques and their application to a consumer behavior study are presented. The Local Majority Method (LOMAME) utilizes a set of prototypes and corrective factors which undergo a training cycle before being utilized as pattern classifiers. Its advantages over the Minimum-Distance Method and the Fix and Hodges Method are discussed in terms of the unique discriminating function. A FORTRAN model of the LOMAME is first applied to standard pattern recognition problems of "A-and-R" and "1101", and shown to be an effective adaptive model for traditional pattern recognition applications. Next, to evaluate its effectiveness in processing behavior pattern data, prototypes and training samples are selected from 200 equipment survey questionnaires returned by members of the Society for Wang Applications and Programs (SWAP). The consumer preference for a FORTRAN-base calculator keyboard over such other keyboards as the traditional calculator and the newer BASIC-base keyboards is studied. The results are promising but considered relatively expensive, time-consuming, and inaccurate in comparison to the performance with the bench-mark problems. Though additional research efforts will undoubtedly improve the direct utility of pattern recognition techniques to consumer survey type applications, a more fundamental use for an adaptive pattern recognizer appears promising. LOMAME prototypes appears to undergo a learning experience that may potentially model the behavioral change of a group of human decision-makers.
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