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https://ieeexplore.ieee.org/document/991432/
Fuzzy support vector machines Abstract: A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes.Cited by: 1589
https://www.researchgate.net/publication/256309499_Fuzzy_Support_Vector_Machines
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849760/
Fuzzy support vector machine is a fuzzy rule-based model in which membership functions are reference functions with location transformation and given input x → determines output class label by equation (9) in which K (x →, z J ⃗) is a Mercer kernel defined by equation (8).Cited by: 10
https://www.sciencedirect.com/science/article/pii/S0952197619301575
Fuzzy least squares Twin Support Vector Machine In many real-world applications, samples in the training data do not strictly belong to a single class. Furthermore, in some applications it is desirable to have different importance degrees for training samples, e.g. in recommender systems newer products should have higher importance degrees than older ones.Author: Javad Salimi Sartakhti, Homayun Afrabandpey, Nasser Ghadiri
https://www.sciencedirect.com/science/article/pii/S0950705116303495
A kernel fuzzy C-means clustering based fuzzy support vector machine algorithm for classification problems with outliers or noises,Cited by: 35
https://stevenschwenke.de/node/project_fuzzy_support_vector_machine
The considered papers contributed to the task of rule extraction from Support Vector Machines (SVM). Therefore, creates SVFI (Support Vector Fuzzy Inference) rules based on support vectors of a given SVM by creating one rule per support vector. A rule consists of n clauses, where n …
https://arxiv.org/pdf/1505.05451v1
Support Vector Machine (SVM) is a classi cation technique based on the idea of Structural Risk Minimization (SRM). It is a kernel-based classi er which was rst introduced in 1995 by Vapnik and his colleagues, at AT&T Bell Labora-Cited by: 7
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