Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29473
Title: Fuzzy kernel hyperball perceptron
Authors: Chung, FL 
Wang, S
Deng, Z
Hu, D
Keywords: Classification
Hyperball
Kernel function
Perceptron
Issue Date: 2004
Publisher: Elsevier
Source: Applied soft computing, 2004, v. 5, no. 1, p. 67-74 How to cite?
Journal: Applied soft computing 
Abstract: In this paper the novel fuzzy kernel hyperball perceptron is presented. The proposed method first maps the input data into a high-dimensional feature space using some Mercer kernel function. Then, the decision function for each class is derived by the learning rules of the fuzzy kernel hyperball perceptron. The fuzzy membership functions, which resolve unclassifiable zones among classes, are incorporated into its classification algorithm to further enhance the perceptron's adaptability and classification accuracy effectively. Unlike SVM, the fuzzy kernel hyperball perceptron has no convergence problem and avoids solving so-called quadratic programming problem which often makes SVM ineffective for large data sets. Especially, unlike the classical SVMs, we can directly utilize the fuzzy kernel hyperball perceptrons to solve multiclass problems, without using any pairwise combination. Our experimental results demonstrate its effectiveness.
URI: http://hdl.handle.net/10397/29473
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2004.03.012
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