Abstract.
Measuring the similarity or distance between sets of points in a metric space is an important problem in machine learning and has also applications in other disciplines e.g. in computational geometry, philosophy of science, methods for updating or changing theories, \(\ldots\). Recently Eiter and Mannila have proposed a new measure which is computable in polynomial time. However, it is not a distance function in the mathematical sense because it does not satisfy the trian gle inequality. We introduce a new measure which is a metric while being computable in polynomial time. We also present a variant which computes a normalised metric and a variant which can associate different weights with the points in the set.
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Received: 18 October 1999 / 8 January 2001
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Ramon, J., Bruynooghe, M. A polynomial time computable metric between point sets. Acta Informatica 37, 765–780 (2001). https://doi.org/10.1007/PL00013304
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DOI: https://doi.org/10.1007/PL00013304