A Fuzzy Classifier with Directed Initialization Adaptive Learning of Norm Inducing Matrix

Author

Yao, Leehter ; Weng, Kuei-Sung ; Chang, Ren-Wei

Author_Institution

Dept. of Electr. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan

fYear

2009

fDate

1-3 April 2009

Firstpage

226

Lastpage

231

Abstract

A fuzzy classifier with adaptive learning of the volume of norm inducing matrix is proposed in this paper. The proposed fuzzy classifier improves the Gustafson-Kessel algorithm (GKA) which assumes a fixed volume of the norm inducing matrix. An efficient approach based on gradient descent learning, called adaptive ellipsoid classification algorithm (AECA) is proposed to recursively update the volume of norm inducing matrix. Mathematical analyses and computer simulations are made to show the effectiveness and efficiency of the proposed fuzzy classifier.

Keywords

fuzzy set theory; gradient methods; matrix algebra; pattern classification; Gustafson-Kessel algorithm; adaptive ellipsoid classification algorithm; adaptive learning; fuzzy classifier; gradient descent learning; norm inducing matrix; Classification algorithms; Clustering algorithms; Database systems; Deductive databases; Ellipsoids; Fuzzy sets; Fuzzy systems; Iterative algorithms; Mathematical analysis; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information and Database Systems, 2009. ACIIDS 2009. First Asian Conference on

Conference_Location

Dong Hoi

Print_ISBN

978-0-7695-3580-7

Type

conf

DOI

10.1109/ACIIDS.2009.57

Filename

5175997