Fuzzy least squares twin support vector machines
نویسندگان
چکیده
منابع مشابه
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two nonparallel hyperplanes are found by solving two systems of linear equations. Although, the algorithm is very fast and efficient in many classification tasks, it is unable to cope with tw...
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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. In this paper, we discuss fuzzy LS-SVMs that resolve unclassifiable ...
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This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classific...
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That is, the system has two symmetric periodic attractors, one of which is shown in Fig. 2(c). In this lemma, we can see an essential function of the ICC that makes stable dynamics by averaging two expanding maps with opposite slopes (d=d x)f (x; 1) > 1, (d=d x)f (x; 01) < 01, and 1=2j(d=d x)f (x; 1) + (d=d x)f (x; 01)j < 1 for x a < j xj < x b. Then Lemma 1 and Lemma 2 guarantee the coexisting...
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ژورنال
عنوان ژورنال: Engineering Applications of Artificial Intelligence
سال: 2019
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2019.06.018