17 December 1996 Two approaches to the sample set condensation: experiments with remote sensing images
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Abstract
The k-NN rules and their modifications offer usually very good performance. The main disadvantage of the k-NN rules is the necessity of keeping the reference set (i.e. training set) in the computer memory. Numerous algorithms for the reference set reduction have been already created. They concern the 1-NN rule and are based on the consistency idea. The 1-NN rule operating with a consistent reduced set classifies correctly, by virtue of consistency, all objects from the original reference set. Quite different approach, based on partitioning of the reference set into some subsets, was proposed earlier by the present authors. The gravity centers of the subsets form the reduced reference set. The paper compares the effectiveness of the two approaches mentioned above. Ten experiments with real data concerning remote sensing data are presented to show the superiority of the approach based on the reference set partitioning idea.
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Adam Jozwik, Adam Jozwik, Sebastiano Bruno Serpico, Sebastiano Bruno Serpico, Fabio Roli, Fabio Roli, "Two approaches to the sample set condensation: experiments with remote sensing images", Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262876; https://doi.org/10.1117/12.262876
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