Pattern classes: A technique for recovering their distributions
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Many statistical pattern-recognition techniques depend for their application on the generation of one or more prototype patterns for each decision class. In turn, the determination of prototypes is dependent on the underlying probability distribution associated with a given class and that distribution's relationship to the distributions associated with the remaining classes. If these distributions are known, the problem of classification is considerably less complex than if they are unknown. The problem of recovering an unknown underlying distribution is one that has received considerable attention. The results thus far, however, are nonpractical. A practical technique that makes use of certain parameters related to sample size is presented and verified.
Key wordsPattern recognition distribution recovery density estimation clustering algorithms fault isolation
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