Kernel Support Vector Machine

Kernel Support Vector Machines (KSVMs) are powerful classification methods that leverage kernel functions to perform nonlinear data separation in high-dimensional feature spaces. Current research focuses on improving the efficiency and scalability of KSVM algorithms, including developing faster optimization techniques like stochastic subgradient methods and exploring the use of quantum kernels for enhanced performance. These advancements are significant because they enable the application of KSVMs to increasingly large and complex datasets, impacting fields like medical diagnosis, high-energy physics, and materials science where accurate and efficient classification is crucial. Furthermore, research is actively exploring optimal hyperparameter selection and feature subset selection methods to improve model accuracy and reduce computational cost.