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Statistical Radial Basis Function Model for Pattern Classification  

Choi Jun-Hyeog (Division of Computer Science, Kimpo College)
Rim Kee-Wook (Knowledge Information & Industrial Engineering Department, Sunmoon Univ.)
Lee Jung-Hyun (School of Computer Science & Engineering, Inha Univ.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.41, no.1, 2004 , pp. 1-8 More about this Journal
Abstract
According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.
Keywords
RBF model; Pattern classification; Support Vector Model; Neural Networks model;
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