Abstract
This paper addresses the problem of matching symbolic descriptions of structured objects. The adopted methodology is the basic component of the decision-making control of a learning system for the acquisition and the refinement of prototypes of visual objects. A vectorial matching evaluation is proposed, as opposed to traditional scalar similarity-measures. This allows the final result of the matching process to account for both the structural similarities of the compared objects, and the information about the reliability of available descriptions. The decision-making mechanism based on such vectorial representation is also described. With regards to the system’s overall flexibility, the advantage of connecting the matching output with the decision-making process via a common vectorial representation is highlighted.
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© 1993 ECSC-EEC-EAEC, Brussels-Luxembourg
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Moneta, C., Vernazza, G., Zunino, R. (1993). A Vectorial Definition of Conceptual Distance for Prototype Acquisition and Refinement. In: Pfleger, S., Gonçalves, J., Vernon, D. (eds) Data Fusion Applications. Research Reports ESPRIT, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84990-9_13
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DOI: https://doi.org/10.1007/978-3-642-84990-9_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56973-2
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