Titlebook: Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines; Theory, Algorithms a Jamal Amani Rad,Kourosh Parand,Sneh

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Fractional Legendre Kernel Functions: Theory and Applicationctions as a kernel. Linear, radial basis functions, and polynomial functions are the most common functions used in this algorithm. Legendre polynomials are among the most widely used orthogonal polynomials that have achieved excellent results in the support vector machine algorithm. In this chapter,

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Fractional Gegenbauer Kernel Functions: Theory and Applicationhine learning issues. Gegenbauer polynomials, like the Chebyshev and Legender polynomials which are introduced in previous chapters, are among the most commonly utilized orthogonal polynomials that have produced outstanding results in the support vector machine method. In this chapter, some essentia

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Solving Ordinary Differential Equations by LS-SVMd on the least squares-support vector machines (LS-SVM) with collocation procedure. One of the most important and practical models in this category is Lane-Emden type equations. By using LS-SVM for solving these types of equations, the solution is expanded based on rational Legendre functions and th

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