The first method is more intuitive, is frequently used in practice, and is the approach that we shall take. We start with a very simple solution, analyze its characteristics, identify its weaknesses, and complicate it only as necessary. In this section we cover the following topics:

- Hypothesize a plausible solution and adjust it to fit the problem

- Create a mathematical model of the problem and derive an optimal classifier

Template matching

Minimum-distance classifiers

Metrics

Inner products

Linear discriminants

Decision boundaries

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