Fuzzy Classification Function of Standard Fuzzy -Means Algorithm for Data with Tolerance Using Kernel Function

被引：0
|
作者：
Kanzawa, Yuchi [1 ]
Endo, Yasunori [2 ]
Miyamoto, Sadaaki [2 ]
机构：
[1] Shibaura Inst Technol, 3-7-5 Toyosu, Tokyo 1358548, Japan
[2] Univ Tsukuba, Tsukuba, Ibaraki 305, Japan
来源：
MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2008年 / 5285卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the fuzzy, classification functions, of the standard fuzzy c-means for data with tolerance using kernel functions are proposed. First, the standard clustering algorithm for data with tolerance using kernel functions are introduced. Second, the fuzzy classification function for fuzzy c-means without tolerance using kernel functions is discussed as the solution of a certain optimization problem. Third, the optimization problem is shown so that the solutions are the fuzzy classification function values for the standard fuzzy c-means algorithms using kernel functions with respect to data with tolerance. Fourth, Karush-Kuhn-Tucker conditions of the objective function is considered, and the iterative algorithm is proposed for the optimization problem. Some numerical examples are shown.
引用
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页码：122 / +
页数：2
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