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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 classes.
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. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that ...Cited by: 1589
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849760/
Experiment 4: as an instance of the rule-base generated by fuzzy support vector machine, we first apply SNR feature selection method on leukemia dataset and select three genes and then we set up fuzzy support vector machine classifier on those genes. We explore some characteristics of the model by taking a deeper look at the rule-base.Cited by: 10
https://www.sciencedirect.com/science/article/pii/S0020025507003209
A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods.Cited by: 131
https://www.sciencedirect.com/science/article/pii/S0952197619301575
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. In many real-world applications, samples may not deterministically be assigned to a single class; they come naturally with their associated uncertainties Also, samples may not be equally important and their importance degrees affect the classification.Author: Javad Salimi Sartakhti, Homayun Afrabandpey, Nasser Ghadiri
https://arxiv.org/pdf/1505.05451v1
Fuzzy Least Squares Twin Support Vector Machines Javad Salimi Sartakhtia,, Nasser Ghadiri a, Homayun Afrabandpey , Narges Yousefnezhadb aDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, IRAN bDepartment of Computer Engineering, Sharif University of Technology, Tehran, 11365-11155, IRAN Abstract Least Squares Twin Support …Cited by: 7
https://www.researchgate.net/publication/311928183_Support_vector_machine_and_fuzzy_logic
The history of the Support Vector Machine (SVM) method and fuzzy logic and its use for classification and regression calculation is described in this [10] paper. Electric vehicles' battery ...
https://www.ijcaonline.org/research/volume131/number3/prakash-2015-ijca-907224.pdf
Fuzzy Support Vector Machines (2007) This paper [3], proposed another classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that tackle some issues that fall in support vector machines (SVMs) related to the face recognition. The proposed TAF-SVM not just tackle overfitting issue came because of the
https://en.wikipedia.org/wiki/Support_vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://dl.acm.org/citation.cfm?id=2326875
Yuancheng Li , Tingjian Fang, Application of fuzzy support vector machines in short-term load forecasting, Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing, May 26-29, 2003, Chongqing, ChinaCited by: 1589
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