Supervised Learning: Classification Using Support Vector Machines (SVM)
Main Idea
Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification or regression tasks. They work by finding the hyperplane in a high-dimensional space that maximizes the margin between the classes. The margin is defined as the distance between the hyperplane and the closest training samples, called support vectors, from either class.
💡 In a two-class classification problem, the hyperplane is a line (in two-dimensional space) or a plane (in three-dimensional space) that separates the data points into two classes. In higher dimensions, the hyperplane is a hyperplane that separates the data points into two classes.
💡 The goal of an SVM is to find the hyperplane that maximizes the margin, which is defined as the distance between the hyperplane and the closest data points from either class, called support vectors. These support vectors determine the position of the hyperplane and the margin. The margin acts as a buffer zone between the classes and helps to reduce overfitting, as the hyperplane tries to maximize the separation between the classes while also avoiding the misclassification of the training samples.
