Minimum-Distance Classifiers

Template matching can easily be expressed mathematically. Let x be the feature vector for the unknown input, and let m1, m2, ..., mc be templates (i.e., perfect, noise-free feature vectors) for the c classes. Then the error in matching x against mk is given by

|| x - mk || .

Here || u || is called the norm of the vector u. A minimum-error classifier computes || x - mk || for k = 1 to c and chooses the class for which this error is minimum. Since || x - mk || is also the distance from x to mk, we call this a minimum-distance classifier. Clearly, a template matching system is a minimum-distance classifier.



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