Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Network

Lim Eng Aik () and Mohd. Syafarudy Abu
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Lim Eng Aik: Institute of Engineering Mathematic Universiti Malaysia Perlis, 02600 Ulu Pauh, Perlis
Mohd. Syafarudy Abu: Institute of Engineering Mathematic Universiti Malaysia Perlis, 02600 Ulu Pauh, Perlis

Scientific Review, 2015, vol. 1, issue 4, 74-78

Abstract: Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.

Keywords: Kernel function; Radial Basis Probabilistic Neural Network; Iris Data; Classification. (search for similar items in EconPapers)
Date: 2015
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