Development of New Pattern-Recognition Methods.
Final rept. 1 Jul 71-19 Jan 73,
STANFORD RESEARCH INST MENLO PARK CALIF
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The problem in pattern recognition is to find a classification or description of the data patterns that matches or suits the data. New methods in pattern recognition are studied in relation to classical approaches using techniques of multivariate statistical analysis. The application of these techniques to specific problems in physical, engineering, behavioral, and other sciences is reviewed. The problems of improved data description and dimensionality reduction are tackled by means of clustering approaches. Several improved clustering methods are developed for general pattern recognition a new approximate procedure for computing the minimal-spanning tree, a new application of the Kolmogorov-Smirnov test for cluster validity, and a new application of relativistic principles in measures of relationship. Experiments using interactive graphic displays to illustrate these new methods are described, and application of computer programs to meteorological problems is demonstrated. Modified author abstract
- Computer Programming and Software