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A new fuzzy support vector machine based on the weighted margin
Qing, T; Jue, W
AbstractThe ideas from fuzzy neural networks and support vector machine (SVM) are incorporated to make SVM classifiers perform better. The influence of the samples with high uncertainty can be decreased by employing the fuzzy membership to weigh the margin of each training vector. The linear separability, fuzzy margin, optimal hyperplane, generalization and soft fuzzy margin algorithms are discussed. A new optimization problem is obtained and SVM is then completely reformulated into a new fuzzy support vector machine (NFSVM). Moreover, the generation bound of NFSVM can be described. We also introduce the membership function in fuzzy neural networks to do some experiments. The results demonstrate that the proposed NFSVM can produce better results than regular SVM and Fuzzy Kernel Perceptron (FKP) in some real cases.
KeywordFuzzy Neural Networks Fuzzy Pattern Recognition Margin Maximal Margin Algorithm Support Vector Machines
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
Indexed BySCI
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000226369600001
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Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China
2.Chinese Acad Sci, Inst Automat, Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Qing, T,Jue, W. A new fuzzy support vector machine based on the weighted margin[J]. NEURAL PROCESSING LETTERS,2004,20(3):139-150.
APA Qing, T,&Jue, W.(2004).A new fuzzy support vector machine based on the weighted margin.NEURAL PROCESSING LETTERS,20(3),139-150.
MLA Qing, T,et al."A new fuzzy support vector machine based on the weighted margin".NEURAL PROCESSING LETTERS 20.3(2004):139-150.
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