Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines
Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines: Theory, Algorithms and Applications (Industrial and Applied Mathematics) by Jamal Amani Rad, Kourosh Parand, Snehashish Chakraverty
English | March 19, 2023 | ISBN: 9811965528 | 319 pages | MOBI | 38 Mb
English | March 19, 2023 | ISBN: 9811965528 | 319 pages | MOBI | 38 Mb
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 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 introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations.
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