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.
Original language | English |
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Title of host publication | The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings |
Pages | 2385-2390 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Event | 2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: 12 Aug 2007 → 17 Aug 2007 |
Conference
Conference | 2007 International Joint Conference on Neural Networks, IJCNN 2007 |
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Country/Territory | United States |
City | Orlando, FL |
Period | 12/08/07 → 17/08/07 |
ASJC Scopus subject areas
- Software