Towards the unification of structural and statistical pattern recognition

Highlights? The unification of statistical and structural pattern recognition has been a concern for a long time. ? We review attempts made towards bridging the gap between both approaches. ? The aim is to profit from the benefits of each approach and eliminate the drawbacks. ? In an experimental evaluation we show the novel procedures are more powerful than approaches from the early period. The field of pattern recognition is usually subdivided into the statistical and the structural approach. Structural pattern recognition allows one to use powerful and flexible representation formalisms but offers only a limited repertoire of algorithmic tools needed to solve classification and clustering problems. By contrast, the statistical approach is mathematically well founded and offers many tools, but provides a representation formalism that is limited in its power and flexibility. Hence, both subfields are complementary to each other. During the last three decades several efforts have been made towards bridging the gap between structural and statistical pattern recognition in order to profit from the benefits of each approach and eliminate the drawbacks. The present paper reviews some of these attempts made towards the unification of structural and statistical pattern recognition and analyzes the progress that has been achieved.

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