Crazy Mathematics behind SVM in a Simple way
SVM, a potent algorithm championed by Vladimir N. Vapnik, triumphed in image classification after being overlooked for three decades. This supervised machine learning tool classifies data points with hyperplanes, excelling in both binary and multilinear classification. SVM’s quest for an optimal hyperplane involves maximizing margin, achieved through Lagrange Multipliers and the Kernel Trick. Though not the primary choice for modern image classification, SVM proves effective for datasets with fewer parameters, showcasing that machine learning at its core is deeply intertwined with mathematics.