Fuzzy Functions With Support Vector Machines

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Fuzzy functions with support vector machines - ScienceDirect

    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

Fuzzy functions with support vector machines Request PDF

    https://www.researchgate.net/publication/222164867_Fuzzy_functions_with_support_vector_machines
    The results indicate that the proposed fuzzy functions with support vector machines approach is a feasible and stable method for regression problems and results in higher performances than the ...

(PDF) Fuzzy Support Vector Machines

    https://www.researchgate.net/publication/256309499_Fuzzy_Support_Vector_Machines
    The high subbands coefficients are classified into two classes by fuzzy support vector machine classification: the texture class and the noise class. ... The fuzzy membership functions from Lin ...

Fuzzy support vector machine: an efficient rule-based ...

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849760/
    In fuzzy support vector machine approach, functions are narrowed to a class of membership functions, which are generated from location transformation of reference functions , and the classifiers defined on them. A membership function µ is a reference function if and only if µ(x) = µ(-x) and µ(0) = 1.Cited by: 10

A fuzzy regression with support vector machine approach to ...

    https://www.sciencedirect.com/science/article/pii/S0360544217301822
    A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. •Cited by: 21

Fuzzy Least Squares Twin Support Vector Machines

    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

Fuzzy least squares support vector machines for multiclass ...

    https://www.sciencedirect.com/science/article/pii/S0893608003001102
    In least squares support vector machines (LS-SVMs), the optimal separating hyperplane is obtained by solving a set of linear equations instead of solving a quadratic programming problem. But since SVMs and LS-SVMs are formulated for two-class problems, unclassifiable regions exist when they are extended to multiclass problems.Cited by: 272

Support-vector machine - Wikipedia

    https://en.wikipedia.org/wiki/Support-vector_machine
    The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, applies the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications. [citation needed

Fuzzy support vector machines for multilabel classification

    https://www.sciencedirect.com/science/article/pii/S003132031500028X
    We develop a fuzzy support vector machine (FSVM) for multilabel classification. • We resolve unclassifiable regions by membership functions. • We also resolve undefined multilabels by membership functions. • Effectiveness of the proposed FSVM is demonstrated by computer experiments.Cited by: 71



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