Support Vector Machine(SVM) is a supervised machine learning algorithm that is usually used in solving binary classification problems. It can also be applied in multi-class classification problems and regression problems. This article represents the mathematics behind the binary-class linear Support Vector Machines. Understanding mathematics helps implement and tune the models in practice. Moreover, you can build your own support vector machine model from scratch, and compare it with the one from Scikit-Learn. For details, you can read this article along with another article of mine.

Specifically, this report explains the key concepts of linear support vector machine, including the primal form and its dual form for both hard margin and soft margin case; the concept of support vectors, max-margin, and the generalization process.

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