Data Science, Machine Learning, Algorithms, Support Vector Machine, Supervised Learning.
functions that define these transformations are called kernels. They work as similarity functions between observations in the training and testing sets. Decision boundary and margin for SVM, along with the corresponding support vectors, using a linear kernel (right) and a polynomial kernel (left).