The basis

• Support vector machine (SVM) is a supervised learning method.
• It can be used for regression or classification purposes.
• SVM is useful for hyper-text categorization, classification of images, recognition of characters…
• The basic visual idea is the creation of planes (lines) to separate features.
• The position of these planes can be adjusted to maximized the margins of the separation of features.
• How we determine which plane is the best? well, it’s done using support vectors.

Support vectors

The support vectors are the dots that determine the planes. The orange and blue dots generate lines and in the middle you can find what is called the hyper-plane.

The minimum distance between the lines created by the support vectors is called the margin.

The diagram above represents the more simple support vector draw you can find, from here you can make it more complex