Statistical Pattern Recognition
Humans can achieve pattern recognition “at a glance” with little apparent effort. Much of pattern recognition is structural, being achieved essentially by analyzing shape. In contrast, statistical pattern recognition treats sets of extracted features as abstract entities that can be used to classify objects on a statistical basis, often by mathematical similarity to sets of features for objects with known classes. This chapter explores the subject, presenting relevant theory where appropriate.
Look out for:
• the nearest neighbor algorithm, which is probably the most intuitive statistical pattern recognition technique.
• Bayes’ theory, which forms the ideal minimum error classification system.
• the relation ...
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