Fuzzy kernel hyperball perceptron

Fu Lai Korris Chung, Wang Shitong, Deng Zhaohong, Hu Dewen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)67-74
Number of pages8
JournalApplied Soft Computing Journal
Issue number1
Publication statusPublished - 1 Dec 2004


  • Classification
  • Hyperball
  • Kernel function
  • Perceptron

ASJC Scopus subject areas

  • Software

Cite this