Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11054
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorDeng, Z-
dc.creatorChung, FL-
dc.creatorWang, S-
dc.date.accessioned2015-09-30T09:43:30Z-
dc.date.available2015-09-30T09:43:30Z-
dc.identifier.isbn978-1-4244-1379-9-
dc.identifier.isbn978-1-4244-1380-5 (E-ISBN)-
dc.identifier.issn1098-7576-
dc.identifier.urihttp://hdl.handle.net/10397/11054-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectComputational geometryen_US
dc.subjectFuzzy set theoryen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectMinimax techniquesen_US
dc.subjectPattern classificationen_US
dc.titleA new minimax probability based classifier using fuzzy hyper-ellipsoiden_US
dc.typeConference Paperen_US
dc.identifier.spage2385-
dc.identifier.epage2390-
dc.identifier.doi10.1109/IJCNN.2007.4371331-
dcterms.abstractIn 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.-
dcterms.bibliographicCitationInternational Joint Conference on Neural Networks, 2007 : IJCNN 2007, 12-17 August 2007, Orlando, FL, p. 2385-2390-
dcterms.issued2007-
dc.relation.ispartofbookInternational Joint Conference on Neural Networks, 2007 : IJCNN 2007, 12-17 August 2007, Orlando, FL-
dc.identifier.rosgroupidr38948-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
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