Support Vector Machine (SVM) is an approach for classification which uses the concept of separating hyperplane. It was developed in the 1990s. It is a generalization of an intuitive and simple classifier called maximal margin classifier. In order to study Support Vector Machine (SVM), we first need to understand what is maximal margin classifier and support vector classifier. In maximal margin classifier, we use a hyperplane to separate the classes.
By Clare Liu, Data Scientist at fintech industry, based in HK. One of the most prevailing and exciting supervised learning models with associated learning algorithms that analyse data and recognise patterns is Support Vector Machines (SVMs). It is used for solving both regression and classification problems. However, it is mostly used in solving classification problems. SVMs were first introduced by B.E. Boser et al. in 1992 and has become popular due to success in handwritten digit recognition in 1994.
The article explains the SVM algorithm in an easy way. Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In simple terms development of algorithms which enable the machine to learn and perform tasks and activities. Over a period of time, many techniques and methodologies were developed for machine learning tasks. In this article, we are going to learn almost everything about one such supervised machine learning algorithm which can be used for both classification and regression(SVR) i.e.
The support vector machine is base on the idea of finding the best line or hyperplane that distinctly classifies the data point. SVM can find the best hyperplane in N- dimensions. Here N is the number of features. For example, if you have two features: A, B then the hyperplane is just a line and if there is three features: A, B, and C, your points will be plotted in the corresponding three-dimensional space based on their values for each independent variable. Support vector machine is a powerful supervised machine learning algorithm.
Support Vector Machines are a popular tool used in several branches of Machine Learning. In particular, they are extremely useful for binary classification. Support Vector Machines have their basis in the concept of separating hyperplanes, so it is useful to first be introduced to this concept. In this article, I introduce the method of classification via separating hyperplanes. We start off simple and describe how even linear regression can be used to make simple binary classifications.