Utilization of singularity exponent in nearest neighbor based classifier |
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Authors: | Marcel Jirina Marcel Jirina Jr |
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Affiliation: | 1. Institute of Computer Science, Pod vodarenskou vezi 2, 182 07, Prague 8 – Liben, Czech Republic 2. Faculty of Biomedical Engineering, Czech Technical University in Prague, Nam. Sítna 3105, 272 01, Kladno, Czech Republic
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Abstract: | Classifiers serve as tools for classifying data into classes. They directly or indirectly take a distribution of data points around a given query point into account. To express the distribution of points from the viewpoint of distances from a given point, a probability distribution mapping function is introduced here. The approximation of this function in a form of a suitable power of the distance is presented. How to state this power—the distribution mapping exponent—is described. This exponent is used for probability density estimation in high-dimensional spaces and for classification. A close relation of the exponent to a singularity exponent is discussed. It is also shown that this classifier exhibits better behavior (classification accuracy) than other kinds of classifiers for some tasks. |
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