January 2019
Intermediate to advanced
294 pages
6h 43m
English
Now we are ready to understand SVMs. SVM is an algorithm that enables us to make use of it for both classification and regression. Given a set of examples, it builds a model to assign a group of observations into one category and others into a second category. It is a non-probabilistic linear classifier. Training data being linearly separable is the key here. All the observations or training data are a representation of vectors that are mapped into a space and SVM tries to classify them by using a margin that has to be as wide as possible:

Let's say there are two classes A and B as in the preceding screenshot.
And from the preceding section, ...