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gj (x) for j = i The minimum probability of misclassi cation is also known as the Bayes error, and a minimum error classi er is also known as a Bayes classi er or a maximum posterior probability (MAP) classi er Below, we brie y discuss three classi ers that approximate the optimal Bayes classi er k-Nearest-Neighbor Classi er The k-nearest-neighbor (kNN) classi er uses all the training features as the set of prototypes {pk } During the testing phase, the distance between each test vector and every prototype is calculated, and the k prototype vectors that are closest to the test vector are identi ed The class labels of these k-nearest prototype vectors are then combined using majority vote or some other method to decide the class label of the test vector When k 1, the kNN classi er is called the nearest-neighbor classi er.

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differences, which we will point out here First, the SPSS macro language does a nice job of standardizing variables, computing the new composite scales, and performing the regression analysis (the SPSS macro syntax for performing all this appears in Appendix C) However, the regression results themselves were in a form that was a bit dif cult for NORM to read directly, so a utility program, NORMSPSSEXE, was created to facilitate the process This utility is available free from our web site The user executes NORMSPSS from the DOS command line and is asked for a few pieces of information along the way In the middle of the largely automatic process, SPSS is invoked, and the analyses are performed on the m (eg, 20) imputed data sets.

It is well-known [6] that asymptotically (in the number of training vectors), the probability of misclassi cation of a nearest-neighbor classi er approaches twice the (optimal) Bayes error Hence, the performance of a nearest-neighbor classi er can be used as a baseline to gauge the performance of other classi ers However, as the number of prototypes increases, a kNN classi er is not very suitable for actual implementation, since it requires too much memory storage and processing power for testing Maximum-Likelihood Classi er Using the Gaussian mixture density model in this classi er, the distribution of training vectors from the same class is modeled as a mixture of Gaussian density functions That is, the likelihood function is modeled as: p(xjvi ) / Gi (xjui ) X.

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