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A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The former option has an implementation advantage since the extractor need only be trained once for use with any classifier, whereas the latter has an advantage since it can be used to minimize classification error directly. Certain criteria, such as Minimum Classification Error, are better suited for simultaneousdoi:10.1109/tpami.2006.186 pmid:16929726 fatcat:a54fcdyoirc7pk3gnibxw5n6si