kernel functions for Support Vector Machines: A Survey

Support Vector Machines (SVMs) are powerful supervised learning algorithms that are extensively used for both classification and regression tasks. An important component of SVMs is the kernel function, which enables the algorithm to perform correctly in nonlinear problems by transforming the input data into higher-dimensional linear spaces. This survey examines various kernel functions employed in Support Vector Machines (SVMs), concentrating on their mathematical formulations, properties, and applications. We examine the most commonly used kernels, including the linear, polynomial, radial basis function (RBF), and sigmoid kernels, and discuss their strengths, weaknesses, and suitable applications. Additionally, we review specialized kernels, such as string kernels and graph kernels, for specific domains, including speech recognition, text mining, and bioinformatics. The paper also addresses kernel selection methods, the impact of kernel parameters on model performance, and challenges in kernel-based learning. This comprehensive overview aims to provide insights into how different kernels can influence SVM performance and guide practitioners in selecting the most suitable kernel for their specific application.