Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11054
Title: A new minimax probability based classifier using fuzzy hyper-ellipsoid
Authors: Deng, Z
Chung, FL 
Wang, S
Keywords: Computational geometry
Fuzzy set theory
Learning (artificial intelligence)
Minimax techniques
Pattern classification
Issue Date: 2007
Publisher: IEEE
Source: International Joint Conference on Neural Networks, 2007 : IJCNN 2007, 12-17 August 2007, Orlando, FL, p. 2385-2390 How to cite?
Abstract: In this paper, a new classifier called minimax-probability based fuzzy hyper-ellipsoid machine (MP-FHM) is proposed. It offers an alternative implementation of the minimax probability based classification with hyper plane and can be taken as an extended version of the ball-model based classifier. By the theorem proposed by Marshall and Qlkin, the training procedure of MP-FHM can be transformed into solving the corresponding unconstrained optimization problems, and thereby various optimization techniques can easily be adopted to solve them. In addition, the MP-FHM can be kernelized, and therefore it has strong nonlinear classification capabilities like other kernel-based classifiers. Various experiments were conducted and the results demonstrate that the proposed classifier is competitive with the state-of-the-art classifiers and is a very promising classification method.
URI: http://hdl.handle.net/10397/11054
ISBN: 978-1-4244-1379-9
978-1-4244-1380-5 (E-ISBN)
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2007.4371331
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

5
Last Week
0
Last month
Citations as of Jul 7, 2017

Page view(s)

27
Last Week
2
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.