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Problem-Solving Models and Search Strategies for Pattern Recognition
February 1979 (vol. 1 no. 2)
pp. 193-201
Noting the major limitations of multivariate statistical classification and syntactic pattern recognition models, this paper presents an overview of some recent work using alternate representations for multistage and nearest neighbor multiclass classification, and for structural analysis and feature extraction. These alternate representations are based on generalizations of state-space and AND/OR graph models and search strategies developed in artificial intelligence (AI). The paper also briefly touches on other current interactions and differences between artificial intelligence and pattern recognition.
Index Terms:
Problem-solving,Pattern recognition,Artificial intelligence,Feature extraction,Pattern analysis,Nearest neighbor searches,Computer Society,Computer science,System testing,Heuristic algorithms,state-space graphs,AND/OR graphs,artificial intelligence,feature extraction,multistage statistical classification,nondirectional structural analysis,pattern recognition,problem solving,search
Citation:
"Problem-Solving Models and Search Strategies for Pattern Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 193-201, Feb. 1979, doi:10.1109/TPAMI.1979.4766905
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