In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.

In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).

Problem Formulation

In SVM, the decision function for predicting data points x corresponds to the equation of an hyperplane:

What is natural language identification?

Language Identification is the task of computationally determining the language of some given piece of text data. A text document could be written entirely in a single language such as English, French, German, Spanish (monolingual language identification), or each text document could have multiple languages in different parts.

Why is language identification important?

Language identification is important for most NLP applications to work accurately, since models are usually trained using data from a single language. If a model is trained on English text and...