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Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

Theory, Algorithms and Applications de

, ,

Éditeur :

Springer


Collection :

Industrial and Applied Mathematics

Paru le : 2023-03-18

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Description

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.
On the other hand, nowadays, the real-time and 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 based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
Pages
305 pages
Collection
Industrial and Applied Mathematics
Parution
2023-03-18
Marque
Springer
EAN papier
9789811965524
EAN PDF
9789811965531
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
30
Taille du fichier
6103 Ko
Prix
147,69 €
EAN EPUB
9789811965531
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
30
Taille du fichier
39557 Ko
Prix
147,69 €

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