The fuzzy quadratic classifier (FQC) extends the quadratic classifier to accommodate a sequence of fuzzy numbers as data. The fuzzy data induces a fuzzy quadratic discriminant (QD) that is then compared to other fuzzy discriminants to produce a decision. To generate the fuzzy data, conversion of batches of data vectors, or signals, into sequences of fuzzy signal vectors is developed base on fuzzy order statistics. In data rich environment, this conversion both condenses and refines the data stream. An efficient implementation of the FQC is presented based upon a diagonal form of the quadratic classifier and robust estimation of the parameters. A two class problem illustrates the procedure.
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