RADIAL BASIS FUNCTION NETWORKS WITH ADJUSTABLE KERNEL SHAPE PARAMETERS

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Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/30771

Title:	RADIAL BASIS FUNCTION NETWORKS WITH ADJUSTABLE KERNEL SHAPE PARAMETERS
Authors:	葉怡成 Yeh, I-Cheng
Contributors:	資訊管理學系 Information Management
Keywords:	Radial basis function network;supervised learning;kernel function;classification
Date:	2010
Issue Date:	2014-06-27 01:10:12 (UTC+8)
Abstract:	Radial basis function network (RBFN) which is commonly used in the classification problems has two parameters, a kernel center and a radius that can be determined by unsupervised or supervised learning. However, it has a disadvantage that it considers tha
Appears in Collections:	[Department of Information Management] Seminar Papers

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