SVM
SVM has been a quite hot topic in recent E-discovery scene. The reason is Relativity has introduced Active Learning which is based on SVM. Now everybody knows it works especially well on classification. However what on earth is SVM? This blog is meant to take you through it briefly so you can know something more than just how to set up an active learning project in Relativity (: Introduction First of all, SVM is a supervised learning algorithm in general, meaning it is often used to classify data which is fundamentally different from LSI. You will need labelled data to train SVM model to predict on unseen data. In a nutshell, SVM is trying to find the hyperplane that can maximize the separation margins. So in another word, there might be many hyperplanes that can separate your data, but SVM is trying to find the best one for you! Nearly all the SVM tutorials will explain what is hyperplane and what is the margins, so i wont discuss it much about them here. The basic SVM is linear ...