Harnessing Kernel Tricks for Nonlinear Problem Solving: SVM Applications
Abstract
Real-world problems often exhibit complex, nonlinear relationships between variables, which cannot be adequately addressed through linear assumptions. This paper explores how advanced algorithms, specifically the kernel trick, transform these nonlinear problems into higher-dimensional spaces, facilitating easier solutions. The mathematical underpinnings of the kernel trick, including the Volterra filter and Hilbert space transformations, are explored. The study implements Support Vector Machines (SVM) with Gaussian kernels to solve the XOR Toy problem, double Fibonacci spiral, and the Breast Cancer Wisconsin dataset, highlighting the efficacy of nonlinear transformations. Experimental results demonstrate high classification accuracy, validating the robust application of kernel methods in complex data analysis.