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http://dx.doi.org/10.5391/JKIIS.2005.15.7.828

Fuzzy Kernel K-Nearest Neighbor Algorithm for Image Segmentation  

Choi Byung-In (한양대학교 전자전기제어계측공학과)
Rhee Chung-Hoon (한양대학교 전자전기제어계측공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.7, 2005 , pp. 828-833 More about this Journal
Abstract
Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.
Keywords
fuzzy K-nearest neighbor; kernel function; nonlinear classification; kernel method;
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