The Classical Model

This leads us to the following classical model for pattern recognition. A system or program called the feature extractor processes the raw data to determine the numerical values for a set of d features x1, x2, ..., xd, which comprise the components of a feature vector x. A system or program called the classifier receives x and assigns it to one of c categories, Class 1, Class 2, ..., Class c.

The design of the feature extractor is very problem dependent. The ideal feature extractor would produce the same feature vector x for all patterns in the same class, and different feature vectors for patterns in different classes. In practice, different inputs to the feature extractor will always produce different feature vectors, but we hope that the within-class variability is small relative to the between-class variability.

At this point, we assume that the designer of the feature extractor has done the best job he or she can, and that the feature vector contains the information needed to distinguish the patterns. Given that feature set, our job is to design the classifier.

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