The Machine Learning Advent Calendar Day 16: Kernel Trick in Excel

A novel approach to Kernel Support Vector Machines (SVM) is introduced by deriving the model from Kernel Density Estimation (KDE), offering a more intuitive understanding of the algorithm. Instead of relying on traditional abstract concepts like kernels and dual formulations, this method constructs the SVM as a sum of localized Gaussian-like functions ("bells") that are iteratively weighted and selected based on hinge loss, ultimately isolating only the most critical data points. This step-by-step process aims to demystify Kernel SVMs and make their mechanics more accessible, potentially enhancing interpretability and implementation, even in environments like