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

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Sherwin Nedaei Janbesaraei,Amirreza Azmoon,Dumitru Baleanuticularly the elites of society (i.e. the winners in life who have excellent incomes, job statuses and educational qualifications) — the integration project may present countless opportunities to draw upon one’s skills and finances. For the vast majority of Europeans, however, contemplating the spec

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Fractional Jacobi Kernel Functions: Theory and Applicationd, the fractional form of Jacobi polynomials will be introduced, and the validation according to Mercer conditions will be proved. Finally, a comparison of the obtained results over a well-known dataset will be provided, using the mentioned kernels with some other orthogonal kernels as well as RBF a

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Book 2023d big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms base

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nalysis of positioning strategies in interaction, the book enhances our understanding of the complex possibilities within processes of self-identification in a migration context.978-3-319-81549-7978-3-319-33566-7