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 |
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