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

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2364-6837 e 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 base978-981-19-6555-5978-981-19-6553-1Series ISSN 2364-6837 Series E-ISSN 2364-6845

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Amir Hosein Hadian Rasanan,Sherwin Nedaei Janbesaraei,Amirreza Azmoon,Mohammad Akhavan,Jamal Amani R

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Solving Integral Equations by LS-SVRilized for developing a numerical algorithm for solving various types of integral equations. The robustness and also the convergence of the proposed method are discussed in this chapter by providing several numerical examples.

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2364-6837 orthogonal kernels.Contains examples that provide a deep int.This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal

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Book 2023 kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is intro

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Introduction to SVMterpretation is given, and its basic concepts and formulations are described. A history of SVM progress (from Vapnik’s primary works in the 1960s up to now) is also reviewed. Finally, various ML applications of SVM in several fields such as medical, text classification, and image classification are presented.