Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

The book "Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines" offers a comprehensive analysis of support vector algorithms from various perspectives. It covers the mathematical foundations and properties of classical kernel functions such as Chebyshev, Legendre, Gegenbauer, and Jacobi. A central theme is the orthogonal kernel functions, whose fractional forms are introduced in several chapters. To facilitate the application of these kernel functions, a tutorial on a Python package called ORSVM is provided. Additionally, various applications of support vector algorithms are presented, which are used not only for classification but also for solving ordinary, partial, integral, and fractional differential equations. Given the growing demand for real-time and big data applications, the parallelization of support vector algorithms using the Compute Unified Device Architecture (CUDA) is also addressed. The book is aimed at researchers in the field of machine learning and scientific computing and offers valuable insights into the use of fractional orthogonal kernel functions.