April 2018
Beginner to intermediate
282 pages
6h 52m
English
SVM is a classifier that works on the principle of separating hyperplanes. Given a training dataset, the algorithms find a hyperplane that maximizes the separation of the classes and uses these partitions for the prediction of a new dataset. The hyperplane is a subspace of one dimension less than its ambient plane. This means the line is a hyperplane for a two-dimensional dataset.