APPROXIMATE SOLUTIONS TO ONE-DIMENSIONAL BACKWARD HEAT CONDUCTION PROBLEM USING LEAST SQUARES SUPPORT VECTOR MACHINES
نویسندگان
چکیده
منابع مشابه
On Approximate Solutions to Support Vector Machines
We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to ex...
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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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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: Journal of the Chungcheong Mathematical Society
سال: 2016
ISSN: 1226-3524
DOI: 10.14403/jcms.2016.29.4.631